departmentofinformationtechnology b.sc

MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGALNH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249

Department of Information TechnologyB.Sc. in Information Technology (Data Science)

1

Semester ISl. No. CBCS

CategoryCourse Code Course Name L T P Credits

Theory + Practical1 CC-1 BITDSC101

BITDSC191Programming Fundamentals 4 0 4 6

2 CC-2 BITDSC102 Discrete Structures 5 1 0 6

3 AECC-1 BITDSA101 Soft skill 2 0 0 24 GE-1 BITDSG101

BITDSG102BITDSG103BITDSG104

1. MOOCS Basket 12. MOOCS Basket 23. MOOCS Basket 34. MOOCS Basket 4

4/5

0/1

4/0

6

Total Credit 20

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Programming FundamentalsCourse Code: BITDSC101 &BITDSC191

Semester: I

Duration: 36 Hrs. MaximumMarks: 100+100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam: 70Tutorial: 0 Attendance : 5Practical: 4 Continuous Assessment: 25Credit: 4 + 2 Practical Sessional internal continuous evaluation: 40

Practical Sessional external examination: 60Aim:Sl. No.

1. Implement your algorithms to build programs in the C programming language

2. Use data structures like arrays, linked lists, and stacks to solve various problems

3. Understand and use file handling in the C programming language

Objective:Sl. No.

1. To write efficient algorithms to solve various problems

2. To understand and use various constructs of the programming language

3. To apply such as conditionals, iteration, and recursion in programming



2

Pre-Requisite:Sl. No.

1. Basic Knowledge of Computer System

Contents Hrs./weekChapter Name of the Topic Hours Marks01 Introduction to Computers

Computer Systems, Computing Environments, ComputerLanguages, Creating and Running Programs, SoftwareDevelopment, Flow charts. Number Systems: Binary, Octal,Decimal, Hexadecimal Introduction to C Language - Background,C Programs, Identifiers, Data Types, Variables, Constants, Input /Output Statements Arithmetic Operators and Expressions:Evaluating Expressions, Precedence and Associativity ofOperators, Type Conversions.

6 10

02 Conditional Control StatementsBitwise Operators, Relational and Logical Operators, If, If- Else,Switch-Statement and Examples. Loop Control Statements: For,While, DoWhile and Examples. Continue, Break and Gotostatements Functions: Function Basics, User-defined Functions,Inter Function Communication, Standard Functions, Methods ofParameter Passing. Recursion- Recursive Functions.. StorageClasses: Auto, Register, Static, Extern, Scope Rules, and TypeQualifiers.

8 10

03 Preprocessors and ArraysPreprocessor Commands Arrays - Concepts, Using Arrays in C,Inter-Function Communication, Array Applications, Two-Dimensional Arrays, Multidimensional Arrays, Linear and BinarySearch, Selection and Bubble Sort.

8 16

04 PointersPointers for Inter-Function Communication, Pointers to Pointers,Compatibility, Lvalue and Rvalue, Arrays and Pointers, PointerArithmetic and Arrays, Passing an Array to a Function, MemoryAllocation Functions, Array of Pointers, ProgrammingApplications, Pointers to void, Pointers to Functions, CommandLine Arguments. Strings - Concepts, C Strings, StringInput/Output Functions, Arrays of Strings, String ManipulationFunctions.

8 16

05 Structures and FileDefinition and Initialization of Structures, Accessing Structures,Nested Structures, Arrays of Structures, Structures and Functions,Pointers to Structures, Self-Referential Structures, Unions, TypeDefinition (typedef), Enumerated Types. Input and Output:Introduction to Files, Modes of Files, Streams, Standard LibraryInput/Output Functions, Character Input/Output Functions.

6 18



3

Sub Total: 36 70Internal Assessment Examination & Preparation of SemesterExamination

4 30

Total: 40 100Practical:Skills to be developed:Intellectual skills:

1. The ability to learn concepts and apply them to other problems. ...

2. Basic mathematical skills.

3. A passion for problem solving.

4. Confidence around a computer programming Language.

List of Practical: Sl. No. 1 to10 compulsory & at least three from the rest)1. Write a c program to display the word "welcome".

2. Write a c program to take a variable int and input the value from the user and displayit.

3. Write a c program to add 2 numbers entered by the user and display theresult.

4. Write a c program to calculate the area and perimeter of acircle.

5. Write a C program to find maximum between twonumbers.

6. Write a C program to check whether a number is divisible by 5 and 11 ornot.

7. Write a C program to input angles of a triangle and check whether triangle is valid ornot.

8. Write a C program to check whether a year is leap year ornot.

9. Write a C program to input basic salary of an employee and calculate its Gross salary according tofollowing:Basic Salary <= 10000 : HRA = 20%, DA = 80%Basic Salary <= 20000 : HRA = 25%, DA = 90%Basic Salary > 20000 : HRA = 30%, DA = 95%

10. Write a c program to print “welcome” 10 times.

11. Write a c program to print first n natural numbers using while loop.

12. Write a c program to print all the odd numbers in a givenrange.

13. Write a c program to add first n numbers using while loop.

14. Write a c program to print all numbers divisible by 3 or 5 in a givenrange.

15. Write a c program to add even numbers in a givenrange.

16. Write a c program to find the factorial of a givennumber.



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17. Write a c program to find whether a number is prime ornot.

18. Write a c program to print the reverse of anumber.

19. Write a c program to add the digits of anumber.

20. Write a c program to print the Fibonacci series in a given range usingrecursion.

21. Write a c program to check whether a number is an Armstrong number ornot.

22. Write a c program to find g.c.d. and l.c.m. of two numbers usingfunction.

Assignments:1. Based on theory lectures.

List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the PublisherYashavantKanetkar, Let us C 13thEdition BPB PublicationE. Balaguruswamy Programming in ANSI

CTata McGraw-Hill

Gary J. Bronson A First Book of ANSI C 4th Edition ACMReference Books:Byron Gottfried Schaum's Outline of

Programming with CMcGraw-Hill

Kenneth A. Reek Pointers on C PearsonBrianW. Kernighanand Dennis M. Ritchie

The C ProgrammingLanguage

Prentice Hall of India

List of equipment/apparatus for laboratory experiments:Sl. No.

1. Computer

End Semester Examination Scheme. MaximumMarks-70. Time allotted-3hrs.Group Unit Objective Questions

(MCQ only with thecorrect answer)

Subjective Questions

No ofquestionto be set

TotalMarks


Toanswer

Marksperquestion

Total Marks

A

B

C

1,2,3,4,5

3, 4, 5

1,2,3,4,5

10 10

5

5

3

3

5

15

60

● Only multiple choice type questions (MCQ) with one correct answer are to be set in the objectivepart.



5

● Specific instruction to the students to maintain the order in answering objectivequestionsshould be given on top of the questionpaper.

Examination Scheme for end semester examination:Group Chapter Marks of each

questionQuestion to beset

Question to beanswered

A All 1 10 10B All 5 5 3C All 15 5 3Examination Scheme for Practical Sessional examination:Practical Internal Sessional Continuous EvaluationInternal Examination:Continuous evaluation 40External Examination: Examiner-Signed Lab Assignments 10On Spot Experiment 40Viva voce 10 60

Name of the Course: B.Sc. in Information Technology (Data Science)Subject:Discrete StructuresCourse Code: BITDSC102 Semester: IDuration: 48 Hrs MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial:1 Attendance: 5Practical:0 Continuous Assessment: 25Credit:6 Practical Sessional internal continuous evaluation: NA

Practical Sessional external examination: NAAim:Sl. No.

1. The aim of this course is to introduce you with a new branch of mathematics which isdiscrete mathematics, the backbone of Computer Science.

2. In order to be able to formulate what a computer system is supposed to do, or to prove thatit does meet its specification, or to reason about its efficiency, one needs the precision ofmathematical notation and techniques. The Discrete Mathematics course aims to providethis mathematical background.

Objective:Throughout the course, students will be expected to demonstrate their understandingofDiscrete Mathematics by being able to do each of the followingSl. No.

1. Use mathematically correct terminology and notation.



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2. Construct correct direct and indirect proofs.

3. Use division into cases in a proof.

4. Use counterexamples.

5. Apply logical reasoning to solve a variety of problems.


1. Knowledge of basic algebra

2. Ability to follow logical arguments.

Contents Hrs./weekChapter

Name of the Topic Hours Marks

01 Set TheoryDefinition of Sets, Venn Diagrams, complements, Cartesianproducts, power sets, counting principle, cardinality andcountability (Countable and Uncountable sets), proofs of somegeneral identities on sets, pigeonhole principle. Relation:Definition, types of relation, composition of relations, domain andrange of a relation, pictorial representation of relation, propertiesof relation, partial ordering relation. Function: Definition and typesof function, composition of functions, recursively definedfunctions.

10 14

02 Propositional logicProposition logic, basic logic, logical connectives, truth tables,tautologies, contradictions, normal forms (conjunctive anddisjunctive), modus ponens and modus tollens, validity, predicatelogic, universal and existential quantification. Notion of proof:proof by implication, converse, inverse, contrapositive, negation,and contradiction, direct proof, proof by using truth table, proof bycounter example.

10 14

03 CombinatoricsMathematical induction, recursive mathematical definitions, basicsof counting, permutations, combinations, inclusion-exclusion,recurrence relations (nth order recurrence relation with constantcoefficients, Homogeneous recurrence relations, Inhomogeneousrecurrence relation), generating function (closed form expression,

10 14



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properties of G.F., solution of recurrence relation using G.F,solution of combinatorial problem using G.F.)

04 Algebraic StructureBinary composition and its properties definition of algebraicstructure, Groyas Semi group, Monoid Groups, Abelian Group,properties of groups, Permutation Groups, Sub Group, CyclicGroup, Rings and Fields (definition and standard results).

8 10

05 GraphsGraph terminology, types of graph connected graphs, componentsof graph, Euler graph, Hamiltonian path and circuits, Graphcoloring, Chromatic number. Tree: Definition, types of tree(rooted,binary), properties of trees, binary search tree, tree traversing(preorder, inorder, post order). Finite Automata: Basic concepts ofAutomation theory, Deterministic finite Automation (DFA),transition function, transition table, Non Deterministic FiniteAutomata (NDFA), Mealy and Moore Machine, Minimization offinite Automation.

10 18


4 30

Total: 52 100Assignments:Based on the curriculum as covered by subject teacher.

List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the PublisherKenneth H. Rosen Discrete Mathematics

and its ApplicationsTata Mc.Graw Hill

eymourLipschutz,M.Lipson

Discrete Mathematics Tata Mc.Graw Hill

Reference Books:V. Krishnamurthy Combinatorics:Theory

and ApplicationsEast-West Press

Kolman, Busby Ross Discrete MathematicalStructures

Prentice Hall International




No of Total No of To Marks Total Marks



8

questionto be set

Marks questionto be set

answer perquestion

A

B

C

1 to 5

1 to 5

1 to 5

10 10

5

5

3

3

5

15

60

● Only multiple choice type question (MCQ) with one correct answer are to be set in theobjectivepart.





A All 1 10 10B All 5 5 3C All 15 5 3

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Soft SkillsCourse Code: BITDSA101 Semester: IDuration: 36 Hrs. MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 2 End Semester Exam: 70Tutorial: 0 Attendance: 5Practical: 2 Continuous Assessment: 25Credit: 2 Practical Sessional internal continuous evaluation: NA


1. Ability to read English with ability to read English with understanding and decipherparagraph patterns, writer techniques and conclusions

2. Skill to develop the ability to write English correctly and master the mechanics of writingthe use of correct punctuation marks and capital letter

3. Ability to understand English when it is spoken in various contexts.

Objective:Sl. No.

1. To enable the learner to communicate effectively and appropriately in real life situation



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2. Touse English effectively for study purpose across the curriculum

3. To use R,W,L,S and integrate the use of four language skills, Reading, writing , listening andspeaking.

4. To revise and reinforce structures already learnt.Pre-Requisite:Sl. No.

1. Basic knowledge of English Language.



01 GrammarCorrection of sentence, Vocabulary/word formation, Single wordfor a group of words, Fill in the blank, transformation of sentences,Structure of sentences – Active / Passive Voice – Direct / IndirectNarration.

6 15

02 EssayWritingDescriptive – Comparative – Argumentative – Thesis statement-Structure of opening/ concluding paragraphs – Body of the essay.

5 5

03 Reading ComprehensionGlobal – Contextual – Inferentialrecommended text.

– Select passages from5 10

04 Business CorrespondenceLetter Writing – Formal.Drafting.Biodata- Resume′- CurriculumVitae.

5 8

05 Report WritingStructure, Types of report – Practice Writing.

5 5

06 Communication skillsPublic Speaking skills, Features ofnonverbal.

effective speech, verbal-5 15

07 Group discussionGroup discussion – principle – practice

5 12


4 30

Total: 40 100

Practical:

Skills to be developed:Intellectual skills:

1. Skill of Grammar



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2. Various writing skills

3. Skill of reading English text

4. Skill of effective written communication

Motor Skills:1. Skill of using Correct body language while giving apresentation

2. Various non-verbal communication skills

3. Skill of using correct gestures and expressions while speakingpublicly

4. Essential approach and attitude in Group Discussion orViva

List of Practical:1. Honing ‘Listening Skill’ and its sub skills through Language Lab Audiodevice.

2. Honing ‘Speaking Skill’ and its sub skills.

3. Helping them master Linguistic/Paralinguistic features (Pronunciation/Phonetics/Voicemodulation/ Stress/ Intonation/ Pitch & Accent) of connected speech.

4. Honing ‘Conversation Skill’ using Language Lab Audio –Visual input, Conversational PracticeSessions (Face to Face / via Telephone, Mobile phone & Role PlayMode).

5. Introducing ‘Group Discussion’ through audio –Visual input and acquainting them with keystrategies for success.

6. GD Practice Sessions for helping them internalize basic Principles (turn- taking, creativeintervention, by using correct body language, courtesies & other soft skills) ofGD.

7. Honing ‘Reading Skills’ and its sub skills using Visual / Graphics/Diagrams /ChartDisplay/Technical/Non Technical Passages, Learning Global / Contextual / InferentialComprehension.

8. Honing ‘Writing Skill’ and its sub skills by using Language Lab Audio –Visual input, PracticeSessions

Assignments:Based on theory lectures.

List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the Publisher



11

R.C. Sharma andK.Mohan

BusinessCorrespondence andReport Writing

Tata McGraw Hill , NewDelhi , 1994

.Gartside Model Business Letters Pitman , London , 1992Reference Books:Mark MaCormack CommunicationJohn Metchell How to write reportsS R Inthira&amp, VSaraswathi

Enrich your English –a) Communicationskills b) Academicskills

CIEFL &amp, OUP

Longman Longman Dictionary ofContemporaryEnglish/OxfordAdvanced Learner’sDictionary of CurrentEnglish

OUP , 1998

Maxwell Nurnbergand RosenblumMorris

All About Words General Book Depot, NewDelhi , 1995

A Text Book for Englishfor Engineers &amp,Technologists


1. Computer

2. Audio Devices

3. Visual Devices

4. Language lab Devices and the dedicated software





TotalMarks


Toanswer

Marksperquestion

Total Marks

A

B

1,2,3,4,5,63, 4, 5, 6

10 10

5 3 5 60



12

C 1,2,3,4,5,6

5 3 15

● Only multiple choice type question (MCQ) with one correct answer are to be set in theobjectivepart.






Name of the Course: B.Sc. in Information Technology (Data Science)Subject:MOOCSCourse Code:BITDSG101/BITDSG102/BITDSG103/BITDSG104

Semester: I

Duration: 36 Hrs. MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: NATutorial: 1 Attendance: 0Practical: 0 Continuous Assessment: 0Credit: 6 Practical Sessional internal continuous evaluation: NA

Practical Sessional external examination: NAContentsStudents will select subjects fromMOOCS Basket which is provided them.



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Semester II

Sl. No. Course Code Course Name L T P Credits


BITDSC291Data Structure and Algorithm withPython

5 1 0 6

2 CC-4 BITDSC202BITDSC292

Operating System 4 0 4 6

3 AECC-2 BITDSA201 Environmental Science 2 0 0 2

4 GE-2 BITDSG201BITDSG202BITDSG203BITDSG204


4/5

0/1

4/0

6

Sessional5 SEC-1 BITDSS281 Project and Entrepreneurship 0 0 4 2

Total Credit 22

Name of the Course: BSc. in Information Technology (Data Science)Subject: Data Structure and Algorithm with PythonCourse Code: BITDSC201 &BITDSC201

Semester: II

Duration: 36 Hrs MaximumMarks:100+100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam:70Tutorial: 0 Attendance: 5Practical: 4 Continuous Assessment: 25Credit: 4+2 Practical Sessional internal continuous evaluation: 40


1. The point of this course is to give you a vibe for algorithms and data structures as afocal area of what it is to be a computer science student.

2. You ought to know about the way that there are regularly a few calculations for someissue, and one calculation might be superior to another, or one calculation better incertain conditions and another better in others.

3. You should have some idea of how to work out the efficiency of an algorithm.

4. You will be able to use and design linked data structures

5. You will learn why it is good programming style to hide the details of a data structure



14

within an abstract data type.6. You should have some idea of how to implement various algorithm using python

programming.Objective:Sl. No.

1. To impart the basic concepts of data structures and algorithms.

2. To understand concepts about searching and sorting techniques.

3. To understand basic concepts about stacks,queues,lists,trees and graphs.

4. To understanding about writing algorithms and step by step approach in solvingproblems with the help of fundamental data structures


1. Basics of programming language.

2. Logic building skills.



01 Introduction to Data StructureAbstract Data Type.

1 2

02 Arrays1D, 2D and Multi-dimensional Arrays, Sparse Matrices.Polynomialrepresentation .

3 4

03 Linked ListsSingly, Doubly and Circular Lists, Normal and Circularrepresentation of Self Organizing Lists, Skip Lists,Polynomial representation.

4 7

04 StacksImplementing single / multiple stack/s in an Array, Prefix, Infixand Postfix expressions, Utility and conversion of theseexpressions from one to another, Applications of stack, Limitationsof Array representation of stack.

4 10

05 QueuesArray and Linked representation of Queue, Circular Queue, De-queue, Priority Queues.

4 7

06 RecursionDeveloping Recursive Definition of Simple Problems and theirimplementation, Advantages and Limitations of Recursion,

4 5



15

Understanding what goes behind Recursion (Internal StackImplementation)

07 TreesIntroduction to Tree as a data structure, Binary Trees (Insertion,Deletion, Recursive and Iterative Traversals of Binary SearchTrees), Threaded Binary Trees (Insertion, Deletion, Traversals),

Height-Balanced Trees (Various operations on AVL Trees).

5 15

08 Searching and SortingLinear Search, Binary Search, Comparison of Linear and BinarySearch, Selection Sort, Insertion Sort, Merge Sort, Quick sort, ShellSort, Comparison of Sorting Techniques

6 15

09 HashingIntroduction to Hashing, Deleting from Hash Table, Efficiency ofRehash Methods, Hash Table Reordering, Resolving collision byOpen Addressing, Coalesced Hashing, Separate Chaining, Dynamicand Extendible Hashing, Choosing a Hash Function, PerfectHashing Function.

5 5


4 30


1. Skill to analyze algorithms and to determine algorithm correctness and their timeefficiency.

2. Knowledge of advanced abstract data type (ADT) and data structures and theirimplementations.

3. Ability to implement algorithms to perform various operations on datastructures.

List of Practical:1. Implementation of array operations.

2. Stacks and Queues: adding, deleting elements .

3. Circular Queue: Adding & deleting elements

4. Merging Problem : Evaluation of expressions operations on Multiple stacks &queues

5. Implementation of linked lists: inserting, deleting, inverting a linked list.

6. Implementation of stacks & queues using linked lists:



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7. Polynomial addition, Polynomial multiplication

8. Sparse Matrices : Multiplication, addition.

9. Recursive and Non Recursive traversal of Trees Threaded binary tree traversal.AVL treeimplementation Application of Trees.

10. Application of sorting and searching algorithms Hash tables implementation: searching,inserting and deleting, searching & sorting techniques.

Assignments:Based on the curriculum as covered by subject teacher.

List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the

PublisherMichael H.Goldwasser,Michael T.Goodrich, andRoberto Tamassia

Data Structures andAlgorithms in Python

1118476735,9781118476734

JohnWiley & Sons

Rance D Necaise Data Structures andAlgorithms UsingPython

9788126562169 JohnWiley & Sons

Reference Books:Sartaj Sahni DataStructures,

Algorithms andapplications in C++

Second Edition Universities Press


1. Computer with moderate configuration

2. Python 2.7 or higher and other softwares as required.





TotalMarks


Toanswer

Marksperquestion

TotalMarks

A 1 to 9 10 10



17

B

C

1 to 9

1 to 9

5

5

3

3

5

15

60

● Only multiple choice type question (MCQ) with one correct answer are to be set in theobjective part.





A All 1 10 10B All 5 5 3C All 15 5 3Examination Scheme for Practical Sessional examination:Practical Internal Sessional Continuous EvaluationInternal Examination:Continuous evaluation 40External Examination: Examiner-Signed Lab Note Book 10On Spot Experiment 40Viva voce 10 60

Name of the Course: B.Sc. in Information Technology (Data Science)Subject:Operating SystemCourse Code: BITDSC202 &BITDSC292

Semester: II

Duration: 36 MaximumMarks: 100+100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam: 70Tutorial: 0 Attendance : 5Practical:4 Continuous Assessment:25Credit: 4+2 Practical Sessional internal continuous evaluation:40

Practical Sessional external examination:60Aim:Sl. No.

1. General understanding of structure of modern computers



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2. Purpose, structure and functions of operating systems

3. Illustration of key OS aspects by example

Objective:Sl. No.

1. To learn the fundamentals of Operating Systems.

2. To learn the mechanisms of OS to handle processes and threads and theircommunication

3. To learn the mechanisms involved in memory management in contemporary OS

4. To gain knowledge on distributed operating system concepts that includesarchitecture, Mutual exclusion algorithms, deadlock detection algorithms andagreement protocols

5. To know the components and management aspects of concurrency management

6. To learn programmatically to implement simple OS mechanisms

Pre-Requisite:Sl. No.1. Strong programming skills (Knowledge of C)

2. Computer architecture

3. Elementary data structures and algorithmsContents Hrs./weekChapter


01 IntroductionConcept of Operating Systems, Generations of Operating systems,Types of Operating Systems, OS Services, System Calls, Structureof an OS - Layered, Monolithic, Microkernel Operating Systems,Concept of Virtual Machine. Case study on UNIX andWINDOWSOperating System.

3 5

02 ProcessesDefinition, Process Relationship, Different states of a Process,Process State transitions, Process Control Block (PCB), Contextswitching Thread: Definition, Various states, Benefits of threads,Types of threads, Concept of multithreads, Process Scheduling:Foundation and Scheduling objectives, Types of Schedulers,Scheduling criteria: CPU utilization, Throughput, TurnaroundTime, Waiting Time, Response Time; Scheduling algorithms: Pre-emptive and Non pre-emptive, FCFS, SJF, RR; Multiprocessorscheduling: Real Time scheduling: RM and EDF.

8 20

03 Inter-process Communication:Critical Section, Race Conditions, Mutual Exclusion, Hardware

4 5



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Solution, Strict Alternation, Peterson’s Solution, The Producer\Consumer Problem, Semaphores, Event Counters, Monitors,Message Passing, Classical IPC Problems: Reader’s & WriterProblem, Dinning Philosopher Problem etc.

04 DeadlocksDefinition, Necessary and sufficient conditions for Deadlock,Deadlock Prevention, Deadlock Avoidance: Banker’s algorithm,Deadlock detection and Recovery.

4 10

05 Memory ManagementBasic concept, Logical and Physical address map, Memoryallocation: Contiguous Memory allocation – Fixed and variablepartition– Internal and External fragmentation and Compaction;Paging: Principle of operation – Page allocation – Hardwaresupport for paging, Protection and sharing, Disadvantages ofpaging. Virtual Memory: Basics of Virtual Memory – Hardwareand control structures – Locality of reference, Page fault ,Working Set , Dirty page/Dirty bit – Demand paging, PageReplacement algorithms: Optimal, First in First Out (FIFO),Second Chance (SC), Not recently used (NRU) and Least Recentlyused (LRU).

8 10

06 I/O HardwareI/O devices, Device controllers, Direct memory access Principlesof I/O Software: Goals of Interrupt handlers, Device drivers,Device independent I/O software, Secondary-Storage Structure:Disk structure, Disk scheduling algorithms File Management:Concept of File, Access methods, File types, File operation,Directory structure, File System structure, Allocation methods(contiguous, linked, indexed), Free-space management (bitvector, linked list, grouping), directory implementation (linearlist, hash table), efficiency and performance.

6 10

07 DiskManagementDisk structure, Disk scheduling - FCFS, SSTF, SCAN, C-SCAN, Diskreliability, Disk formatting, Boot-block, Bad blocks.

3 10


4 30


1. Can be able to Identify the purpose of theanalysis.2. Can be considered a reliable source of information.3. Can able to use a variety of techniques to extend the original idea.



20

List of Practical:1. Basics of UNIX commands.2. Shell programming3. Implementation of CPU scheduling. a) Round Robin b) SJF c) FCFS d)Priority4. Implement all file allocation strategies5. Implement Semaphores6. Implement Bankers algorithm for Dead LockAvoidance7. Implement an Algorithm for Dead LockDetection9. Implement the all page replacement algorithms a) FIFO b) LRU c) LFU 10. Implement Sharedmemory and IPC10. Implement Paging Technique f memorymanagement.11. Implement Threading & SynchronizationApplicationsAssignments:Based on the curriculum as covered by subject teacher.List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the

PublisherAviSilberschatz, PeterGalvin, Greg Gagne,

Wiley Asia

Operating SystemConcepts Essentials 978-1-119-32091-3

William Stallings Operating Systems:Internals and Design

Principles

5th Edition Prentice Hall of India

Reference Books:Charles Crowley Operating System: A

Design-orientedApproach

1st Edition Irwin Publishing

J. Nutt, Addison-Wesley

Operating Systems: AModern Perspective

2nd Edition

Maurice Bach Design of the UnixOperating Systems

8th Edition Prentice-Hall of India

Daniel P. Bovet,Marco Cesati

Understanding theLinux Kernel

3rd Edition O'Reilly andAssociates


1. Computer

2. Linux/Ubantu operating system


(MCQ only with theSubjective Questions



21

correct answer)No ofquestionto be set

TotalMarks


Toanswer

Marks perquestion

TotalMarks

A

B

C

1 to 7

1 to 7

1 to 7

1010

5

5

3

3

5

15

60







Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Environmental ScienceCourse Code:BITDSA201 Semester: II

Duration: 36 Hrs MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 2 End Semester Exam: 70Tutorial:0 Attendance: 5Practical:0 Continuous Assessment: 25Credit: 2 Practical Sessional internal continuous evaluation: NA

Practical Sessional external examination: NAAim:Sl. No.1. To enable critical thinking in relation to environmental affairs.



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2. Understanding about interdisciplinary nature of environmental issues

3. Independent research regarding environmental problems in form of project report

Objective:Sl. No.1. To create awareness about environmental issues.

2. To nurture the curiosity of students particularly in relation to natural environment.

3. To develop an attitude among students to actively participate in all the activitiesregarding environment protection

4. To develop an attitude among students to actively participate in all the activitiesregarding environment protection



01 IntroductionBasic ideas of environment, basic concepts, man, society &amp,environment, their interrelationship. Mathematics of populationgrowth and associated problems, Importance of population studyin environmental engineering, definition of resource, types ofresource, renewable, non- renewable, potentially renewable, effectof excessive use vis-à-vis population growth, SustainableDevelopment.Materials balance: Steady state conservation system, steady statesystemwith non-conservative pollutants, step function.Environmental degradation: Natural environmental Hazards likeFlood, earthquake, Landslide-causes, effects andcontrol/management, Anthropogenic degradation like Acid rain-cause, effects and control. Nature and scope of EnvironmentalScience and Engineering.

3 10

02 EcologyElements of ecology: System, open system, closed system,definition of ecology, species, population, community, definition ofecosystem- components types and function.Structure and function of the following ecosystem: Forestecosystem, Grassland ecosystem, Desert ecosystem, Aquaticecosystems, Mangrove ecosystem (special reference to Sundarban), Food chain [definition and one example of each food chain],Food web.Biogeochemical Cycle- definition, significance, flow chart ofdifferent cycles with only elementary reaction [Oxygen, carbon,Nitrogen, Phosphate, Sulphur].Biodiversity- types, importance, Endemic species, Biodiversity Hot-spot, Threats to biodiversity, Conservation of biodiversity.

7 10



23

03 Air pollution and controlAtmospheric Composition: Troposphere, Stratosphere,Mesosphere, Thermosphere,Tropopause and Mesopause. Energybalance:Conductive and Convective heat transfer, radiation heattransfer, simple global temperature model [Earth as a black body,earth as albedo], Problems.Green house effects: Definition, impactof greenhouse gases on the global climate and consequently on seawater level, agriculture and marine food.Global warming and itsconsequence, Control of Global warming. Earth’s heatbudget. Lapse rate: Ambient lapse rate Adiabatic lapse rate,atmospheric stability, temperature inversion (radiation inversion).Atmospheric dispersion: Maximum mixing depth, ventilationcoefficient, effective stack height, smokestack plumes and Gaussianplume model. Definition of pollutants and contaminants, Primaryand secondary pollutants: emission standard, criteria pollutant.Sources and effect of different air pollutants- Suspendedparticulate matter, oxides of carbon, oxides of nitrogen, oxides ofsulphur, particulate, PAN. Smog, Photochemical smog and Londonsmog. Depletion Ozone layer: CFC, destruction of ozone layer byCFC, impact of other green house gases, effect of ozonemodification. Standards and control measures: Industrial,commercial and residential air quality standard, control measure(ESP. cyclone separator, bag house, catalytic converter, scrubber(ventury), Statement with brief reference).

6 10

04 Water Pollution and ControlHydrosphere, Hydrological cycle and Natural water. Pollutants ofwater, their origin and effects: Oxygen demanding wastes,pathogens, nutrients, Salts, thermal application, heavy metals,pesticides, volatile organic compounds. River/Lake/ground waterpollution: River: DO, 5 day BOD test, Seeded BOD test, BODreaction rate constants, Effect of oxygen demanding wastes onriver[deoxygenation, reaeration], COD, Oil, Greases, pH. Lake:Eutrophication [Definition, source and effect]. Ground water:Aquifers, hydraulic gradient, ground water flow (Definition only)Standard and control: Waste water standard [BOD, COD, Oil,Grease], Water Treatment system [coagulation and flocculation,sedimentation and filtration, disinfection, hardness and alkalinity,softening] Wastewater treatment system, primary and secondarytreatments [Trickling filters, rotating biological contractor,Activated sludge, sludge treatment, oxidation ponds] tertiarytreatment definition. Water pollution due to the toxic elements andtheir biochemical effects: Lead, Mercury, Cadmium, and Arsenic.

6 15

05 Land PollutionLithosphere, Internal structure of earth, rock and soil 1L Solid

4 10



24

Waste: Municipal, industrial,commercial, agricultural, domestic, pathological and hazardoussolid wastes, Recovery anddisposal method- Open dumping, Land filling, incineration,composting, recycling. Solidwaste management and control (hazardous and biomedical waste).

06 PollutionDefinition of noise, effect of noise pollution, noise classification[Transport noise, occupational noise, neighbourhood noise]Definition of noise frequency, noise pressure, noise intensity, noisethreshold limit value, equivalent noise level,(18hr Index), Ldn.Noise pollution control.

5 10

07 Environmental ManagementEnvironmental impact assessment, Environmental Audit,Environmental laws and protection act of India, Differentinternational environmental treaty/ agreement/ protocol.

5 5


4 30

Total: 40 100Name of Author Title of the Book Edition/ISSN/ISBN Name of the

PublisherG. M.Masters, Introduction to

EnvironmentalEngineering andScience

Prentice-Hall of IndiaPvt. Ltd., 1991

Reference Books:A. K. De Environmental

ChemistryNew Age International





TotalMarks


Toanswer

Marksperquestion

TotalMarks

A

B

C

1 to 5

1 to 5

1 to 5

10 10

5

5

3

3

5

15

60

● Only multiple choice type question (MCQ) with one correct answer are to be set inthe



25

objective part.





A All 1 10 10B All 5 5 3C All 15 5 3


Semester: II

Duration: Min 8Weeks MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: NATutorial: 1 Attendance: 0Practical: Continuous Assessment: 0Credit: 6 Practical Sessional internal continuous evaluation: NA


Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Project and EntrepreneurshipCourse Code: BITDSS281 Semester: IIDuration: 48 Hrs. MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 0 End Semester Exam: 100Tutorial: 0 Attendance: 0Practical: 4 Continuous Assessment: 0Credit: 2 Practical Sessional internal continuous evaluation: 40



26

Practical Sessional external examination: 60ContentsStudents will do projects on application areas of latest technologies and current topics of societalrelevance.

Semester IIISl.No.

CBCSCategory

Course Code Course Name L T P Credits


BITDSC391Database Management System 4 0 4 6

2 CC-6 BITDSC302 Foundation of Data Science 4 0 4 63 CC-7 BITDSC303 Data Mining & Data Warehousing 5 1 0 64 GE-3 BITDSG301

BITDSG302BITDSG303BITDSG304


4/5

0/1

4/0

6

5 SEC-2 BITCSS381 Object Oriented Programming 1 0 4 3Total Credit 27

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Database Management SystemCourse Code: BITDSC301 &BITDSC391

Semester: III



1. To store and transform data into information

2. To organize the data in the form of table, schema and report forms

3. To provide security of data



27

4. Data is stored in either hierarchical form or a navigational form

Objective:Sl. No.

1. Understand the uses the database schema and need for normalization

2. Experience with SQL

3. Use different types of physical implementation of database

4. Use database for concurrent use

Pre-Requisite:Sl. No.3. Elementary knowledge about computers including some experience using UNIX or

Windows4. Computer Programming & Utilization



01 Database system architectureData Abstraction, Data Independence, Data Definition Language(DDL), Data Manipulation Language (DML). Data models: Entity-relationship model, network model, relational and objectoriented data models, integrity constraints, data manipulationoperations.

6 15

02 Relational query languagesRelational algebra, Tuple and domain relational calculus, SQL3,DDL and DML constructs, Open source and Commercial DBMS -MYSQL, ORACLE, DB2, SQL server. Relational database design:Domain and data dependency, Armstrong's axioms, Normalforms, Dependency preservation, Lossless design. Queryprocessing and optimization: Evaluation of relational algebraexpressions, Query equivalence, Join strategies, Queryoptimization algorithms.

12 25

03 Storage strategiesIndices, B-trees, hashing.

6 10

04 Transaction processingConcurrency control, ACID property, Serializability ofscheduling, Locking and timestamp based schedulers, Multi-version and optimistic Concurrency Control schemes, Databaserecovery.

8 15

05 Advanced topicsObject oriented and object relational databases, Logicaldatabases, Web databases, Distributed databases, Datawarehousing and datamining.

4 5



28


4 30

Total: 40 100Practical:


1. Can be able to implement the plan .2. Can be able to use a variety of techniques to extend the original idea.3. Can be able to analyze relevant data.4. Can be considered valid by the fact of it.

List of Practical: Sl. No. 1& 2 compulsory & at least three from the rest)1. Design a Database and create required tables. For e.g. Bank, CollegeDatabase2. Apply the constraints like Primary Key , Foreign key, NOT NULL to thetables.3. Write a sql statement for implementing ALTER,UPDATE andDELETE4. Write the queries to implement the joins5. Write the query for implementing the following functions:MAX(),MIN(),AVG(),COUNT()6. Write the query to implement the concept of Intergrityconstrains7. Write the query to create the views8. Perform the queries for triggers9. Perform the following operation for demonstrating the insertion , updation and deletion

using the referential integrity constraints.10. Write the query for creating the users and their role.

Assignments:Based on the curriculum as covered by subject teacher.List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the

PublisherAbrahamSilberschatz, HenryF. Korth, S.Sudarshan

Database SystemConcepts

6th Edition McGraw-Hill

R. Elmasri and S.Navathe

Fundamentals ofDatabase Systems

5th Edition Pearson Education

Reference Books:J. D. Ullman Principles of Database

and Knowledge – BaseSystems

Computer SciencePress

Abiteboul, RichardHull, Victor Vianu,Addison-Wesley

Foundations ofDatabases



29

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Foundation of Data Science


1. Computer/Laptop

2. Oracle /Mysql





TotalMarks


Toanswer

Marks perquestion

TotalMarks

A

B

C

1 to 5

1 to 5

1 to 5

10 10

5

5

3

3

5

15

60


● Specific instruction to the students to maintain the order in answeringobjectivequestions should be given on top of the questionpaper.




A All 1 10 10B All 5 5 3C All 15 3 3Examination Scheme for Practical Sessional examination:Practical Internal Sessional Continuous EvaluationInternal Examination:Continuousevaluation

40

External Examination: Examiner-Signed Lab Note Book 10On Spot Experiment 40Viva voce 10 60



30

Course Code: BITDSC302 Semester: IIIDuration: 36 MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial: 1 Attendance : 5Practical:0 Continuous Assessment:25Credit: 6 Practical Sessional internal continuous evaluation:NA

Practical Sessional external examination:NAAim:Sl. No.1 To gain basic knowledge of data and information.

2 To gain basic knowledge of data science.

3 To understand the history, potential application area and future of data science.

4 To gain basic knowledge of machine learning.

Objective:Sl. No.1 Provide you with the knowledge and expertise to become a proficient data scientist.

2 Demonstrate an understanding of statistics and machine learning concepts that arevital for data science;

3 Produce Python code to statistically analyse a dataset;

4 Critically evaluate data visualisations based on their design and use forcommunicating stories from data;


1 Knowledge of basic mathematics.

2 Analytical and Logical skills



01 Introduction: What is Data Science? Big Data and Data Science –Datafication - Current landscape of perspectives - Skill setsneeded; Matrices - Matrices to represent relations betweendata, and necessary linear algebraic operations on matrices -



31

Approximately representing matrices by decompositions (SVDand PCA); Statistics: Descriptive Statistics: distributions andprobability - Statistical Inference: Populations and samples -Statistical modeling - probability distributions - fitting a model -Hypothesis Testing - Intro to R/ Python.

02 Data preprocessing: Data cleaning - data integration - DataReduction Data Transformation and DataDiscretization.Evaluation of classification methods – Confusionmatrix, Students T-tests and ROC curves-Exploratory DataAnalysis - Basic tools (plots, graphs and summary statistics) ofEDA, Philosophy of EDA - The Data Science Process.

03 Basic Machine Learning Algorithms: Association Rule mining -Linear Regression- Logistic Regression - Classifiers - k-NearestNeighbors (k-NN), k-means -Decision tree - Naive Bayes-Ensemble Methods - Random Forest. Feature Generation andFeature Selection - Feature Selection algorithms - Filters;Wrappers; Decision Trees; Random Forests.

04 Clustering: Choosing distance metrics - Different clusteringapproaches - hierarchical agglomerative clustering, k-means(Lloyd's algorithm), - DBSCAN - Relative merits of each method -clustering tendency and quality.

Data Visualization: Basic principles, ideas and tools for datavisualization.


4 30

Total: 40 100

Assignments:Based on the curriculum as covered by subject teacher.List of BooksText Books:



32

Name of Author Title of the Book Edition/ISSN/ISBN Name of thePublisher

Cathy O'Neil andRachel Schutt

Doing Data Science,Straight Talk From TheFrontline

O'Reilly, 2014.

Jiawei Han,Micheline Kamberand Jian Pei

Data Mining: Conceptsand Techniques

Third Edition. ISBN0123814790, 2011

Jure Leskovek,AnandRajaramanand Jeffrey Ullman

Mining of MassiveDatasets. v2.1

Cambridge UniversityPress

Reference Books:Kevin P. Murphy Machine Learning: A

ProbabilisticPerspective

ISBN 0262018020

Foster Provost andTom Fawcett

Data Science forBusiness: What YouNeed to Know aboutData Mining and Data-analytic Thinking

ISBN 1449361323.2013

Trevor Hastie,Robert Tibshiraniand JeromeFriedman

Elements of StatisticalLearning

Second Edition. ISBN0387952845. 2009.(free online)





TotalMarks


Toanswer

Marks perquestion

TotalMarks

A

B

1 to 5

1 to 5

10 10

5 3 560



33

C 1 to 5 5 3 15● Only multiple choice type question (MCQ) with one correct answer are to be set in the

objective part.● Specific instruction to the students to maintain the order in answeringobjective

questions should be given on top of the questionpaper.




A All 1 10 10B All 5 5 3C All 15 3 3Examination Scheme for Practical Sessional examination:Practical Internal Sessional Continuous EvaluationInternal Examination:Continuousevaluation

40

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Data Mining & Data WarehousingCourse Code: BITDSC303 Semester: IIIDuration: 48 MaximumMarks:100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial: 1 Attendance : 5Practical:0 Continuous Assessment:25Credit: 6 Practical Sessional internal continuous evaluation:NA

Practical Sessional external examination:NAAim:Sl. No.

1. Understand the functionality of the various data mining and data warehousingcomponent

2. Appreciate the strengths and limitations of various data mining and data warehousingmodels

Objective:Sl. No.1. Be familiar with mathematical foundations of data mining tools..

2. Understand and implement classical models and algorithms in data warehouses anddata mining

3. Characterize the kinds of patterns that can be discovered by association rule mining,classification and clustering.



34

4. Master data mining techniques in various applications like social, scientific andenvironmental context.

5. Develop skill in selecting the appropriate data mining algorithm for solving practicalproblems.


1. Knowledge of DBMS

2. Analytical Knowledge



01Introduction to Data Warehousing; Data Mining: Miningfrequent patterns, association and correlations; SequentialPattern Mining concepts, primitives,scalablemethods;

8 10

02 Classification and prediction; Cluster Analysis – Types of Data inCluster Analysis, Partitioning methods, Hierarchical Methods;Transactional Patterns and other temporal based frequentpatterns,

8 10

03Mining Time series Data, Periodicity Analysis for time relatedsequence data, Trend analysis, Similarity search in Time-seriesanalysis;

8 10

04Mining Data Streams, Methodologies for stream data processingand stream data systems, Frequent pattern mining in streamdata, Sequential Pattern Mining in Data Streams, Classificationof dynamic data streams, Class Imbalance Problem; GraphMining; Social Network Analysis;modulation forcommunication, filtering, feedback control systems.

11 20

05Web Mining, Mining the web page layout structure, mining weblink structure, mining multimedia data on the web, Automaticclassification of web documents and web usage mining;Distributed Data Mining.

9 10

06Recent trends in Distributed Warehousing and Data Mining,Class Imbalance Problem; Graph Mining; Social NetworkAnalysis.

4 10


4 30

Total: 52 100



35

Practical:Skills to be developed:Intellectual skills:

1. Explain the analyzing techniques of various data2. Describe different methodologies used in data mining and data warehousing3. Compare different approaches of data ware housing and data mining with various

technologies.4. Can use a variety of techniques to extend the original idea.


PublisherPaulraj Ponniah DataWarehousing

Fundamentals for ITProfessionals

Wiley India

Alex Berson andStephen J. Smith

DataWarehousing,Data Mining, & OLAP

Second Edition Tata McGraw HillEducation

Reference Books:Ralph Kimball Data warehouse

ToolkitWiley India

Jiawei Han and MKamber

Data Mining Conceptsand Techniques

Second Edition Elsevier Publication

G Dong and J Pei Sequence Data Mining SpringerEnd Semester Examination Scheme. MaximumMarks-70. Time allotted-3hrs.Group Unit Objective Questions




TotalMarks


Toanswer

Marks perquestion

TotalMarks

A

B

C

1 to 6

1 to 6

1 to 6

1010

5

5

3

3

5

15

60



Examination Scheme for end semester examination:Group Chapter Marks of each Question to be Question to be



36

question set answeredA All 1 10 10B All 5 5 3C All 15 3 3


Semester: III

Duration: 36 Hrs. MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: NATutorial: 1 Attendance: 0Practical: Continuous Assessment: 0Credit: 6 Practical Sessional internal continuous evaluation: NA


Name of the Course: B.Sc. in Information Technology (Data Science)

Subject: Object-Oriented ProgrammingCourse Code: BITCSS381

Semester: III

Duration: 12 MaximumMarks: 100+100

Teaching Scheme Examination Scheme

Theory: 1 End Semester Exam: 70

Tutorial: 0 Attendance : 5

Practical:4 Continuous Assessment:25

Credit: 4+2 Practical Sessional internal continuous evaluation:40



37

Practical Sessional external examination:60

Aim:

Sl. No.

1. To understand Basic concepts of OOPs

2. To Learn programming by class and object model

3. Get knowledge Java programming

Objective:

Sl. No.

1. To learn the fundamentals of Java programming such as data types, variables andarrays.

2. To study the syntax and necessity of decision making and iterative statements.

3. To create a class and invoke the methods.

4. To instigate programming in overloading of methods.

5. To emphasize the concept of packages.

6. To learn the exception handling routines.

Pre-Requisite:

Sl. No.

1. The fundamental point in learning programming

2. Basic knowledge of algorithms and procedural programming

Contents Hrs./week

Chapter


01 Introduction: 4 20



38

Why object orientation, History and development of objectoriented programming language, concepts of object orientedprogramming language. Difference between OOP and otherconventional programming – advantages and disadvantages. Datatypes, variables. Array, operators. String, I/O. Control statements.Object oriented design: Major and minor elements, classfundamentals. Declaring objects, instantiation of class, introducingmethods. Constructing objects using constructor. Static variable,constants. Visibility modifiers.

02 Object Properties:

Introduction to basic features of a class (encapsulation,polymorphism etc) Data field encapsulation. Passing objects tomethods. Array of objects, 'This' keyword Relationships amongobjects: aggregation, composition, dependency, links. Relationshipamong classes: association, aggregation. Meta class, meta object.Grouping constructs.

4 25

03 Basic concepts of object oriented programming using Java:

Using objects as parameters, closure look at argument passing,returning objects. Introducing access control, Final keyword,garbage collection, Nested and inner classes. Class abstraction andencapsulation, Overloading of methods (overloading ofconstructor). Super class, subclasses, super keyword, inheritance,types, member access. Multilevel hierarchy, process of constructorcalling in inheritance. Overriding methods, overriding vs.overloading, polymorphism. Abstract class, interface & comparisonbetween abstract class and interface Packages, importingpackages. Exception handling basics, types, using try &catch,throw, throws & finally. Threading, synchronization &priorities,thread class, creating thread. Basic applet programming. Life cycle.

4 25

Sub Total: 12 70

Internal Assessment Examination & Preparation of SemesterExamination

4 30

Total: 18 100

Practical:

Skills to be developed:



39

Intellectual skills:

1. Students will be able to implement basic data structure and control statements inobject oriented programming.

2. Student will be able to design class with its basic features.3. Students can write programs using Java to implementOOP4. Student will be able to design object oriented programs with the concept of object,

class, abstraction, encapsulation, inheritance etc. to provide flexibility, modularity andre-usability in programming.

5. They can also be able to design Meta classes and groupingconstruct.

List of Practical:

1. Introduction to Java and JDK2. Java Fundamentals - Data Types, Control Loops3. Java Fundamentals - Wrapper Classes, Arrays4. Classes and Objects 5 Inheritance5. Abstract Class & Interface6. File I/O and ExceptionHandling7. Graphical User Interface (GUI) Programming with Java Swing8. Applets9. Java Threads

Assignments:

Based on the curriculum as covered by subject teacher.

List of Books

Text Books:


Rambaugh, JamesMichael, Blaha

Object OrientedModelling and Design

Prentice Hall

Patrick Naughton,Herbert Schildt

The completereference-Java2

TMH

Reference Books:

Sourav Sahay ”Object-Oriented Oxford



40

Programming with C++

Blaha, Rumbaugh Object-OrientedModeling and Design

with UML

Pearson Ed

. Ali Bahrami Object OrientedSystem Development

Mc Graw Hill

List of equipment/apparatus for laboratory experiments:

Sl. No.

1. Computer

2. JDK

End Semester Examination Scheme. MaximumMarks-70. Time allotted-3hrs.

Group Unit Objective Questions




TotalMarks


Toanswer

Marks perquestion

TotalMarks

A

B

C

1 to 3

1 to 3

1 to 3

10

10

5

5

3

3

5

15

60





41

Examination Scheme for end semester examination:

Group Chapter Marks of eachquestion

Question to beset


A All 1 10 10

B All 5 5 3

C All 15 3 3

Examination Scheme for Practical Sessional examination:

Practical Internal Sessional Continuous Evaluation

Internal Examination:

Continuous evaluation 40

External Examination: Examiner-

Signed Lab Note Book 10

On Spot Experiment 40

Viva voce 10 60

Semester IVSl.No.

CBCSCategory



BITDSC491Computer Networks 4 0 4 6


Software Engineering 4 0 4 6

3 CC-10 BITDSC403 Machine Learning for Data Science 5 1 0 64 GE-4 BITDSG401 1. MOOCS Basket 1

2. MOOCS Basket 23. MOOCS Basket 34. MOOCS Basket 4

4/5

0/1

4/0

6

Sessional6 SEC-3 BITDSS481 Minor Project and Entrepreneurship I 0 0 4 4



42

Total Credit 28

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Computer NetworkingCourse Code: BITDSC401 &BITDSC491

Semester: IV

Duration: 36 Hrs. MaximumMarks: 100+100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam: 70Tutorial: 0 Attendance : 5Practical: 4 Continuous Assessment: 25Credit: 4 + 2 Practical Sessional internal continuous evaluation: 40


1. To gain Knowledge of uses and services of Computer Network

2. To enhance Ability to identify types and topologies of network.

3. To gain Understanding of analog and digital transmission of data.

Objective:Sl. No.1. To deliver comprehensive view of Computer Network.

2. To enable the students to understand the Network Architecture, Network type and topologies

3. To understand the design issues and working of each layer of OSI model.

4. To familiarize with the benefits and issues regarding Network Security.


1. Basic Knowledge of Computer SystemContents Hrs./weekChapter Name of the Topic Hours Marks01 Introduction

Introduction to communication systems, Data, signal andTransmission: Analog and Digital, Transmission modes, components,Transmission Impairments, Performance criteria of a communicationsystem. Goals of computer Network, Networks: Classification,

3 10



43

Components and Topology, categories of network [LAN,MAN,WAN];Internet: brief history, internet today; Protocols andstandards; OSI and TCP/IP model.

02 Data link layer:Types of errors, framing [character and bit stuffing], error detection& correction methods; Flow control; Protocols: Stop & wait ARQ

6 10

03 Medium access sub layer:Point to point protocol, FDDI, token bus, token ring; Reservation,polling, concentration; Multiple access protocols: ALOHA,CSMA,FDMA, TDMA, CDMA; Ethernet

4 10

04 Network layer: Internetworking & devices: Repeaters, Hubs, Bridges,Switches, Router, Gateway; Addressing : Internet address, classfuladdress,Routing : techniques,static vs. dynamic routing ,Protocols:IP, IPV6

6 10

05 Transport layer:Process to process delivery; UDP; TCP; Congestion control algorithm:Leaky bucket algorithm, Token buc ket algorithm, Quality of services[Qos]

6 10

06 Application LayerDNS, SMTP, FTP, HTTP & WWW; Security: Cryptography [Public,Private Key based], Digital Signature, Firewalls [technology &applications]

6 10

07 Physical Layer:Overview of data[analog & digital], signal[analog &digital],transmission [analog & digital] & transmission media [guided &unguided]; Circuit switching: time division & space division switch,TDM bus; Telephone Network

5 10


4 30

Total: 40 100Practical:List of Practical: Implementation of practicals are adhered to the theoretical curriculum.Assignments:Based on the curriculum as covered by subject teacher.List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the PublisherB. A. Forouzan Data Communications

and NetworkingTMH

A. S. Tanenbaum Computer Networks Pearson Education/PHIW. Stallings Data and Computer

CommunicationsPHI/ Pearson Education



44

Reference Books:


1 Computer with moderate configuration2 Network simulator package





TotalMarks


Toanswer

Marksperquestion

Total Marks

A

B

C

1-7

1-7

1-7

10 10

5

5

3

3

5

15

60







Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Software EngineeringCourse Code: BITDSC402 & Semester: IV



45

BITDSC492Duration: 36 Hrs. MaximumMarks: 100+100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam: 70Tutorial: 0 Attendance : 5Practical: 4 Continuous Assessment: 25Credit: 4 + 2 Practical Sessional internal continuous evaluation: 40


1. Familiarization with the concept of software engineering and its relevance.

2. Understanding of various methods or models for developing a software product.

3. Ability to analyze existing system to gather requirements for proposed system

4. Gain skill to design and develop software.

Objective:Sl. No.1. To introduce the students to a branch of study associated with the development of a software

product.2. To gain basic knowledge about the pre-requisites for planning a software project.

3. To learn how to design of software

4. To enable the students to perform testing of a software


2. Basic Knowledge of Computer System

Contents Hrs./weekChapter Name of the Topic Hours Marks01 Overview of Computer Based Information System- TPS, OAS, MIS,

DSS, KBS Development Life Cycles- SDLC and its phases ModelsWaterfall, Prototype, Spiral, Evolutionary Requirement Analysis andSpecification, SRS System analysis- DFD, Data Modeling with ERD

12 20

02 Feasibility Analysis System design tools- data dictionary, structurechart, decision table, decision tree. Concept of User Interface,Essence of UML. CASE tool.

7 15

03 Testing- Test case, Test suit, Types of testing- unit testing, systemtesting, integration testing, acceptance testing Designmethodologies: top down and bottom up approach, stub, driver,black box and white box testing.

7 20



46

04 ERP, MRP, CRM, Software maintenance SCM, concept of standards[ISO and CMM]

10 15


4 30

Total: 40 100Practical:Assignments:Based on the curriculum as covered by subject teacher.List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the PublisherIgor Hawryszkiewycz System analysis and

designPEARSON

V Rajaraman Analysis and design ofInformation System

PHI

Ian Sommerville Software Engineering Addison-WesleyReference Books:

List of equipment/apparatus for laboratory experiments:Sl. No.1. Computer





TotalMarks


Toanswer

Marksperquestion

Total Marks

A

B

C

1,2,3,4,5

3, 4, 5

1,2,3,4,5

10 10

5

5

3

3

5

15

60



Examination Scheme for end semester examination:Group Chapter Marks of each Question to be Question to be



47

question set answeredA All 1 10 10B All 5 5 3C All 15 5 3Examination Scheme for Practical Sessional examination:Practical Internal Sessional Continuous EvaluationInternal Examination:Continuous evaluation 40External Examination: Examiner-Signed Lab Assignments 10On Spot Experiment 40Viva voce 10 60

Name of the Course: B.Sc. in Information Technology (Data Science)Subject:Machine Learning for Data ScienceCourse Code: BITDSC403 &BITDSC493

Semester: IV



1. To learn R

2. To introduce the basic concepts and techniques of Machine Learning

3. To develop the skills in using recent machine learning software for solving practicalproblems

Objective:Sl. No.

1. To expose to basic terms and terminologies of Machine Learning.

2. To study the various algorithms related to supervised and unsupervised learning.

3. To understand the different types of Machine Learning models and how to use them.


1. Strong programming skills (Knowledge of C)

2. Data computational skill



48



01 Introduction To RIntroduction to mechanism for statistics, data analysis, andmachine learning; Introduction of R Programming, How toinstall and run R, Use of R help files, R Sessions, R ObjectsVectors, Attributes, Matrices, Array, Class, List, Data Frames etc.Operators in R.R Programming Structures, Control Statements, Loops, Repeatand Break, R-Function, RVector Function, Recursive Function inR.R Packages (Install and Use), Input/Output Features in R,Reading or Writing in File. Data Manipulation in R. Rearrangingdata, Random Number and Simulation, Statistical methods likemin, max, median, mean, lengthR Programming Structures, Control Statements, Loops, Repeatand Break, R-Function, RVector Function, Recursive Function inR.

3 5

02 Supervised Learning (Regression/Classification)Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes.Linear models: Linear Regression, Logistic Regression,Generalized Linear ModelsSupport Vector Machines, Nonlinearity and Kernel MethodsBeyond Binary Classification: Multi-class/Structured Outputs,Ranking

8 15

03 Unsupervised LearningClustering: K-means/Kernel K-meansDimensionality Reduction: PCA andkernel PCA Matrix Factorization andMatrix Completion Generative Models(mixture models and latent factormodels)

4 10

04 Evaluating Machine Learning algorithms and Model Selection,Introduction to Statistical Learning Theory, Ensemble Methods(Boosting, Bagging, RandomForests)

410

05 Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep Learning and Feature RepresentationLearning

8 10



49

06 Scalable Machine Learning (Online and Distributed Learning)A selection from some other advanced topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning,Inference in Graphical Models, Introduction to BayesianLearning and Inference

6 10

07 Recent trends in various learning techniques of machinelearning and classification methods

3 10


4 30

Total: 40 100


1. Identify the purpose of the analysis.2. To describe the relationship between factors of theanalysis.3. Information can be useful, used to create new things to achieveobjective.4. Can use a variety of techniques to extend the original idea.

List of Practical:1. Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesisbased on a given set of training data samples. Read the training data from a .CSVfile.2. For a given set of training data examples stored in a .CSV file, implement and demonstrate theCandidate-Elimination algorithm to output a description of the set of all hypotheses consistentwith the training examples.3. Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use anappropriate data set for building the decision tree and apply this knowledge to classify a newsample. 4. Build an Artificial Neural Network by implementing the Back propagation algorithmand test the same using appropriate data sets.5. Write a program to implement the naïve Bayesian classifier for a sample training data setstored as a .CSV file. Compute the accuracy of the classifier, considering few test datasets.6. Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier modelto perform this task. Built-in Java classes/API can be used to write the program. Calculate theaccuracy, precision, and recall for your data set.7. Write a program to construct a Bayesian network considering medical data. Use this model todemonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You can useJava/Python ML library classes/API.8. Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set forclustering using k-Means algorithm. Compare the results of these two algorithms and comment onthe quality of clustering. You can add Java/Python ML library classes/API in theprogram.9. Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Printboth correct and wrong predictions. Java/Python ML library classes can be used for thisproblem.



50

10. Implement the non-parametric Locally Weighted Regression algorithm in order to fit datapoints. Select appropriate data set for your experiment and draw graphs.



PublisherJoseph Adler R in a Nutshell OreillyKevin Murphy Machine Learning: A

ProbabilisticPerspective

MIT Press

Reference Books:Trevor Hastie, RobertTibshirani, Jerome

Friedman

The Elements ofStatistical Learning

Springer

Christopher Bishop Pattern Recognitionand Machine Learning

Springer

Jared P. Lander R for Everyone:Advanced Analyticsand Graphics

Paperback


1. Computer

2. R software





TotalMarks


Toanswer

Marks perquestion

TotalMarks

A

B

C

1 to 7

1 to 7

1 to 7

1010

5

5

3

3

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15

60




51







Semester: IV

Duration: 36 Hrs. MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: NATutorial: 1 Attendance: 0Practical: 0 Continuous Assessment: 0Credit: 6 Practical Sessional internal continuous evaluation: NA


Name of the Course: B.Sc. in Information Technology (Data Science)Subject:Minor Project and Entrepreneurship ICourse Code: BITDSS481 Semester: IVDuration: 48 Hrs. MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 0 End Semester Exam: 100



52

Tutorial: 0 Attendance: 0Practical: 4 Continuous Assessment: 0Credit: 4 Practical Sessional internal continuous evaluation: 40


Semester VSl.No.

CBCSCategory



BITDSC591Internet of Things 4 0 4 6


Artificial Intelligence 4 1 4 6

3 DSE-1 BITDSD501BITDSD591

Elective-I 4/5

0/1

4/0

6

A. Deep LearningB. Descriptive AnalyticsC. Real Time AnalyticsD. Natural Language Processing

4 DSE-2 BITDSD502 Elective-II 4/5

0/1

4/0

6A. Translational BioinformaticsB. Information and Coding

TheoryC. Predictive & Prognostic

AnalyticsD. Optimisation Techniques in

Data AnalysisSessional

5 SEC-4 BITDSS501 Industrial Training and Internship 0 0 0 2Total Credit 26



53

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Internet of ThingsCourse Code: BITDSC501 &BITDSC591

Semester: V

Duration: 36 Hours MaximumMarks: 100 + 100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam: 70Tutorial: 0 Attendance : 5Practical: 4 Continuous Assessment: 25Credit: 4 + 2 Practical Sessional internal continuous evaluation: 40

Practical Sessional external examination: 60Aim:Sl. No.1. Able to realize the revolution of Internet in Mobile Devices, Cloud & Sensor

Networks

2 Able to understand the application areas of IOT

3 Able to understand building blocks of Internet of Things and characteristics

Objective:Sl. No.1. To Understand the vision of IoT from a global context.

2 To Determine the Market perspective of IoT.

3 To Use of Devices, Gateways and Data Management in IoT.

4 To Application of IoT in Industrial and Commercial Building Automation andReal World Design Constraints.

5 To Building state of the art architecture in IoT.

Pre-Requisite:Sl. No.1. Fundamentals of Programming

2. Mathematics

3 Digital Electronics

Contents Hrs./week



54

Chapter Name of the Topic Hours Marks01 INTRODUCTION TO IoT

Introduction to IoT - Definition and Characteristics, PhysicalDesign Things- Protocols, Logical Design- Functional Blocks,Communication Models- Communication APIs Introduction tomeasure the physical quantities, IoT Enabling Technologies –Wireless Sensor Networks, Cloud Computing Big Data Analytics,Communication Protocols- Embedded System- IoT Levels andDeployment Templates.

8 15

02 IoT & M2MMachine to Machine, Difference between IoT and M2M,Software define NetworkChallenges in IoTDesign challenges, Development challenges, Security challenges,Other challenges

8 15

03 IoT PROGRAMMINGIntroduction to Smart Systems using IoT - IoT DesignMethodology- IoT Boards (Rasberry Pi, Arduino) and IDE - CaseStudy: Weather Monitoring- Logical Design using Python, Datatypes & Data Structures- Control Flow, Functions- Modules-Packages, File Handling - Date/Time Operations, Classes- PythonPackages of Interest for IoT.

12 25

04 Domain specific applications of IoTHome automation, Industry applications, Surveillanceapplications, Other IoT applications

8 15


30

Total: 100Practical

List of Practical:1. As compatible to theory syllabus.


List of Books



55

Text Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the

PublisherYasuura, H., Kyung,C.-M., Liu, Y., Lin, Y.-L.

Smart Sensors at theIoT Frontier

SpringerInternationalPublishing

ArshdeepBahga andVijay Madisetti

Internet of Things:Hands-on Approach,

Hyderabad UniversityPress, 2015.

KazemSohraby,Daniel Minoli andTaiebZnati

Wireless SensorNetworks:Technology. Protocolsand Application

Wiley Publications,2010.

Reference Books:Kyung, C.-M., Yasuura,H., Liu, Y., Lin, Y.-L.

Smart Sensors andSystems

Springer InternationalPublishing

Edgar Callaway Wireless SensorNetworks: Architectureand Protocols

Auerbach Publications,2003.

Holger Karl andAndreas Willig

Protocols andArchitectures forWireless SensorNetworks

John Wiley & Sons Inc.,2005

Carlos DeMoraisCordeiro andDharmaPrakashAgrawal

Ad Hoc and SensorNetworks: Theory andApplications

World ScientificPublishing, 2011

List of equipment/apparatus for laboratory experiments:Sl. No.1. Computer ,Different sensor




No ofquestion to

TotalMarks

No ofquestion to

To answer Marks perquestion

TotalMarks



56

be set be setA

B

C

1 to 5

1 to 5

1 to 5

10 10

5

5

3

3

5

15

70

● Only multiple choice type question (MCQ) with one correct answer are to be set in the objectivepart.● Specific instruction to the students to maintain the order in answering objective questions should be

given on top of the question paper.




A All 1 10 10B All 5 5 3C All 15 5 3Examination Scheme for Practical Sessional examination:Practical Internal Sessional Continuous EvaluationInternal Examination:Five No of Experiments

External Examination: Examiner-Signed Lab Note Book(for fiveexperiments)

5*2=10

On Spot Experiment(one for eachgroup consisting 5 students)

10

Viva voce 5

Name of the Course: B.Sc. in Information Technology (Data Science)Subject:Artificial Intelligence

Course Code: BITDSC502 &BITDSC592

Semester: V





57

Objective:Sl. No.

1.To learn the difference between optimal reasoning Vs human like reasoning

2. To understand the notions of state space representation, exhaustive search, heuristicsearch along with the time and space complexities

3. To learn different knowledge representation techniques

4. To understand the applications of AI: namely Game Playing, Theorem Proving, ExpertSystems, Machine Learning and Natural Language Processing


1.

2.



01 UNIT-IIntroduction: What is AI? Foundations of AI, History of AI, Agents andenvironments, The nature of the Environment, Problem solvingAgents, Problem Formulation, Search Strategies

8 10

02 UNIT-IIKnowledge and Reasoning: Knowledge-based Agents, Representation,Reasoning and Logic, Prepositional logic, First-order logic, Using First-order logic, Inference in First-order logic, forward and BackwardChaining

8 20

03 UNIT-IIILearning: Learning from observations, Forms of Learning, InductiveLearning, Learning decision trees, why learning works, Learning inNeural and Belief networks

6 15

04 UNIT-IVPractical Natural Language Processing: Practical applications, Efficientparsing, Scaling up the lexicon, Scaling up the Grammar, Ambiguity,Perception, Image formation, Image processing operations for Earlyvision, Speech recognition and Speech Synthesis

615



58

05 UNIT-VRobotics: Introduction, Tasks, parts, effectors, Sensors, Architectures,Configuration spaces, Navigation and motion planning, Introductionto AI based programming Tools

8 10


4 30

Total: 40 100Practical:List of Practical: Hands-on experiments related to the course contentsAssignments:Based on the curriculum as covered by subject teacher.List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the

PublisherStuart Russell, Peter

NorvigArtificial Intelligence: AModern Approach

2nd Edition, PearsonEducation, 2007

B. Yagna Narayana Artificial NeuralNetworks

PHI

Reference Books:E.Rich and K.Knight

(TMH).Artificial Intelligence 2nd Edition

Simon Haykin Neural Networks PHIPatterson PHI. Artificial Intelligence

and Expert Systems


1. Computer with high configuration

2. Python / Matlab/R





TotalMarks


Toanswer

Marksperquestion

TotalMarks

A 1 to 5 1010 60



59

B

C

1 to 5

1 to 5

5

5

3

3

5

15● Only multiple choice type question (MCQ) with one correct answer are to be set in the

objective part.






Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Deep LearningCourse Code: BITDSD501A &BITDSD591A

Semester: V

Duration: 36 MaximumMarks: 100+100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam: 70Tutorial: 0 Attendance : 5Practical:4 Continuous Assessment:25Credit: 4+2 Practical Sessional internal continuous evaluation: 40


1. To improve the performance of a Deep Learning model

2. to the reduce the optimization function which could be divided based on theclassification and the regression problems

Objective:Sl. No.



60

1. To acquire knowledge on the basics of neural networks.

2. To implement neural networks using computational tools for variety of problems.

3. To explore various deep learning algorithms.


1. Calculus, Linear Algebra

2. Probability & Statistics

3. Ability to code in R/Python



01 IntroductionVarious paradigms of earning problems, Perspectives andIssues in deep learning framework, review of fundamentallearning techniques.

3 5

02Feed forward neural network

Artificial Neural Network, activation function, multi-layerneural network,cardinality, operations, and properties of fuzzyrelations.

6 10

03 Training Neural NetworkRisk minimization, loss function, backpropagation,regularization, model selection, and optimization.

6 15

04 Conditional Random FieldsLinear chain, partition function, Markov network, Beliefpropagation, Training CRFs, Hidden Markov Model, Entropy.

915

05 Deep LearningDeep Feed Forward network, regularizations, training deepmodels, dropouts, Convolutional Neural Network, RecurrentNeural Network, Deep Belief Network.

6 15

06 Deep Learning researchObject recognition, sparse coding, computer vision, naturallanguage

6 10


4 30

Total: 40 100



61


1. Can be able to analyze relevant data.2. Can be able to identify a solution for theproblem.3. Can be able to provide the basis for the analysis.

List of Practical:Practical based on theory paper Deep Learning


PublisherGoodfellow,

I.,Bengio,Y., andCourville A.,

Deep Learning MIT Press

Satish Kumar Neural Networks: AClassroom Approach

Tata McGraw-Hill

Reference Books:Bishop, C. ,M. Pattern Recognition

and Machine LearningSpringer

Yegnanarayana, B. Artificial NeuralNetworks

PHI Learning Pvt. Ltd

Golub, G.,H., and VanLoan,C.,F.

Matrix Computations JHU Press


3. Computer





TotalMarks


Toanswer

Marks perquestion

TotalMarks

A

B

C

1 to 6

1 to 6

1 to 6

1010

5

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62






A All 1 10 10B All 5 5 3C All 15 3 3Examination Scheme for Practical Sessional examination:

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Descriptive Analytics

Course Code: BITDSD501B Semester: VDuration: 36 MaximumMarks: 100+100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam: 70Tutorial: 0 Attendance : 5Practical:4 Continuous Assessment:25Credit: 4+2 Practical Sessional internal continuous evaluation:40


1. To interpretation of historical data to better understand.

2. Make decision by obtain analysis of data.

Objective:Sl. No.

1. To understand the four measurement scales

2. To interpret the utilization of mean values to describe group results.

3. To identify the areas of strength and weakness in an organization.


1. Programming skills (Knowledge of R)

2. Elementary knowledge of data structures and algorithms



63



01 Introduction to R SoftwareBasics and R as a Calculator. Calculation with Data Vectors.Built-in Commands and Missing Data Handling, Operation withMatrices. Introduction to descriptive statistics, AbsoluteFrequency, Relative Frequency, Frequency Distribution andCumulative Distribution Function.

8 15

02 Graphics and Plots, Bar DiagramSubdivided Bar, Pie Diagrams, Histogram, Kernel Density andStem - Leaf Plots.Central tendency of Data, Arithmetic Mean, Median, Quantiles,Mode, Geometric Mean and Harmonic Mean, Range,Interquartile Range and Quartile Deviation.

10 20

03 Variation in Data

Absolute Deviation and Absolute Mean Deviation, Mean SqureError, Variance and Standard Deviation, Coeffivient of Variationand Boxplots.Moments, Association of Variables, Raw and Central Moments.Sheppard’s Correction, Absolute Moments and computation ofmoments, Skewness and Kurtosis.

8 20

04 Association of VariablesUnivariate and Bivariate Scatter Plots, Smooth Scatter Plots,Quantile and Three Dimensional Plots, Correlation Coefficient,Rank Correlation Coefficent, Measures of Association of Discreteand counting Variables, Least Squre Method

1015


4 30

Total: 40 100Practical:Skills to be developed:Intellectual skills:1. Can provide the basis for the analysis.2. Can determine the cause of the problem.3. Can improve the solution to theproblem.

List of Practical:Data exploration (histograms, bar chart, box plot, line graph, scatter plot)



64

Qualitative and Quantitative Data

Measure of Central Tendency (Mean, Median and Mode),

Measure of Positions (Quartiles, Deciles, Percentiles and Quantiles),

Measure of Dispersion (Range, Median, Absolute deviation about median, Variance and Standarddeviation), Anscombe's quartet Other Measures: Quartile and Percentile, Interquartile Range


PublisherJohn Fox An R Companion to

Applied RegressionSecond Edition Sage Publications

Reference Books:Phil Spector Data Manipulation with

RSpringer

John Fox Applied RegressionAnalysis andGeneralized LinearModels

Sage Publications

Robert A. Muenchen,Joseph Hilbe

R for Stata Users Springer


1. Computer

2. R software





TotalMarks


Toanswer

Marks perquestion

TotalMarks

A

B

C

1 to 4

1 to 4

1 to 4

1010

5

5

3

3

5

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60




65





A All 1 10 10B All 5 5 3C All 15 3 3

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Real Time Analytics

Course Code: BITDSD501C &BITDSD591C

Semester: V

Duration: 36 Hrs MaximumMarks:100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam:70Tutorial: 0 Attendance: 5Practical: 4 Continuous Assessment: 25Credit: 6 Practical Sessional internal continuous evaluation: 40


5. To be processed and analyzed as they arrive in real time

6. Learn business case studies for big data analytics.

7. It is important in situations where real-time processing and analysis can deliverimportant insights and yield business value

Objective:Sl. No.

5. Understand the fundamentals of real time streaming data.

6. Understand how to process real time data and store them.

7. To visualize real time data


3. Database Management Systems.

4. Object Oriented Programming Through Java



66



01 Introduction to Streaming DataSource of streaming data, why streaming data is different,infrastructure and algorithms

6 10

02Designing Real-Time Streaming ArchitecturesReal time architecture components, features of a real timearchitecture, language of real time programming, real timearchitecture checklist, Maintaining distributed states, apachezookeeper

10 20

03 Data FlowManagement, processing and storing in StreamingAnalysisDistributed data flows, apache kafka, apache flumeDistributed Processing Streaming Data, Strome, Samza, Consitenthashing, NoSQL and other technologies

12 20

04 Analysis and VisualizationDelivering Streaming Metrics,Exact Aggregation andDelivery,Statistical Approximation of Streaming DataApproximating Streaming Data with Sketching BeyondAggregation

8 20


4 30

Total: 40 100


1. Ability to implement algorithms to perform various operations on strome, smaza

2. Ability to process real time streaming data

List of Practical:Hand on experiments based on theory paper


List of BooksText Books:



67


Wily Real time Analytics Byron EllisReference Books:Anand Rajaramanand Jeffrey DavidUllman

Mining ofDatasets

Massive CUP

TomWhite Hadoop: The DefinitiveGuide

Third Edition O’reilly Media

Chris Eaton, Dirk Understanding Big McGrawHillDeRoos, Tom Data: Analytics for PublishingDeutsch, George Enterprise ClassLapis, Paul Hadoop and StreamingZikopoulos DataPete Warden Big Data Glossary O’ReillyList of equipment/apparatus for laboratory experiments:Sl. No.


4. Linux os or VM

5. Hadoop 2.x or higher and other software as required.

End Semester Examination Scheme.3hrs.

MaximumMarks-70. Time allotted-

Group Unit Objective Questions(MCQ only with thecorrect answer)



TotalMarks


Toanswer

Marksperquestion

TotalMarks

A 1 to 4 10 10

B 1 to 4 5 3 5 60

C 1 to 4 5 3 15● Only multiple choice type question (MCQ) with one correct answer are to be set in the

objective part.







68

A All 1 10 10B All 5 5 3C All 15 5 3

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Natural Language Processing

Course Code: BITDSD501D &BITDSD591D

Semester: V

Duration: 48 MaximumMarks: 100+100Teaching Scheme Examination SchemeTheory: 4 End Semester Exam: 70Tutorial: 0 Attendance : 5Practical: 4 Continuous Assessment:25Credit: 4+2 Practical Sessional internal continuous evaluation:40


1. Process the text data at syntactic and semantic level.

2. Extract the ¬key information from Text data.

3. Analyze the text content to provide predictions related to a specific domainusing language models.

Objective:Sl. No.

1. To get introduced to language processing technologies for processing the text data

2. To understand the role of Information Retrieval and Information Extraction inText Analytics.

3. To acquire knowledge on text data analytics using language models.


1. Programming Knowledge





69

01 Regular Expressions and Automata Recap-Introduction to NLP, Regular Expression, Finite State AutomataTokenization –Word Tokenization, Normalization, Sentence Segmentation,Named Entity Recognition, Multi Word Extraction, Spell Checking –Bayesian Approach, Minimum Edit DistanceMorphology –Inflectional and Derivational Morphology, Finite StateMorphological Parsing, The Lexicon and Morphotactics,Morphological Parsing with Finite State Transducers, OrthographicRules and Finite State Transducers, Porter Stemmer

12 20

02 Language ModelingIntroduction to N-grams, Chain Rule, Smoothing – Add-OneSmoothing, Witten-Bell Discounting; Backoff, DeletedInterpolation, N-grams for Spelling and Word Prediction,Evaluation of languagemodels.HiddenMarkov Models and POS TaggingMarkov Chain, Hidden Markov Models, Forward Algorithm, ViterbiAlgorithm, Part of Speech Tagging – Rule based and MachineLearning based approaches, Evaluation.

12 20

03 Text ClassificationText Classification, Naïve Bayes’ Text Classification, Evaluation,Sentiment Analysis – Opinion Mining and Emotion Analysis,Resources and Techniques.Context Free GrammarContext Free Grammar and Constituency, Some common CFGphenomena for English, Top-Down and Bottom-up parsing,Probabilistic Context Free Grammar, Dependency Parsing

12 20

04 Computational Lexical Semantics Introduction to Lexical Semantics – Homonymy, Polysemy,Synonymy, Thesaurus – WordNet, Computational LexicalSemantics – Thesaurus based and Distributional Word SimilarityInformation Retrieval Boolean Retrieval, Term-documentincidence, The Inverted Index, Query Optimization, PhraseQueries, Ranked Retrieval – Term Frequency – Inverse DocumentFrequency based ranking, Zone Indexing, Query term proximity,Cosine ranking, Combining different features for ranking, SearchEngine Evaluation, Relevance Feedback

12 10


4 30

Total: 52 100List of Practical:Hand on experiments based on theory paper



70


PublisherJurafsky and Martin, Speech and Language

ProcessingPearson Education

Manning and Schutze Foundation ofStatistical Natural

Language Processing

MIT Press

Reference Books:Multilingual NaturalLanguage ProcessingApplications fromTheory to Practice

Bikel, Pearson

Matthew A. Russell Mining the Social Web O'ReillyEnd Semester Examination Scheme. MaximumMarks-70. Time allotted-3hrs.Group Unit Objective Questions




TotalMarks


Toanswer

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1 to 4

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1 to 4

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A All 1 10 10B All 5 5 3C All 15 3 3



71

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Translational BioinformaticsCourse Code: BITDSD502A Semester: VDuration: 36 MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial: 1 Attendance : 5Practical:0 Continuous Assessment:25Credit: 6 Practical Sessional internal continuous evaluation: NA


1. To provide an elementary knowledge in Bioinformatics and Biological Information onthe web.

Objective:Sl. No.

1. To enable the students to understand scope of Bioinformatics

2. Understanding of popular bioinformatics database

3. Learn Fundamentals of Databases and Sequence alignment

4. Approaches to drug discovery using bioinformatics techniques


1. Programming Knowledge(such as C)



01 Introduction to bioinformatics● Biological databases, with main focus on DNA and protein

sequences

● Comparison and alignment of sequences, similarity-basedsearches in databases

● Discovery of protein sequence motifs and sequence features;metabolic pathway data

Genome browsers and sources of gene expression data; genelists and the concept of enrichment

Micro-RNAs and their targets; protein visualization

8 10

02 Phylogenetics 8 20

http://bioinfoacademy.com/IntroBioinfo

http://bioinfoacademy.com/Phylogenetics



72

Introduction to phylogenetics, and essentials of evolution asbackground

Data types for phylogenetic analysis and parsimony

Distance based methods, distance matrices, nucleotidesubstitution models

Model based methods: maximum likelihood and Bayesianphylogenetics

Auxiliary methods: bootstrapping, consensus trees, treecomparison

Visualization of phylogenetic trees

03 Structural bioinformatics

Basics of protein structures and structure determination.Simple validation of models by Ramachandran plots. Basic useof molecular graphics software.

Molecular graphics: illustrating and highlighting moleculardetails on screen and print; generating molecular surfaces.

Comparison of structures: overlaying molecules and measuringtheir structural similarity

Molecular animations

Theory of protein modeling and protein dynamics

Validation and analysis of models and project work.

6 15

04 Biological data analysis with R

Introduction to R: Installation, package management, basicoperations

Sequences and sequence analysis

Annotating gene groups: Ontologies, pathways, enrichmentanalysis

Proteomics: mass spectometry

Reconstructing gene regulation networks

Network analysis: iGraph

6 10

http://bioinfoacademy.com/StructBioi

http://bioinfoacademy.com/BioDataAnalysis



73

05 High-throughput data analysis with R

Flow cytometry: counting and sorting stained cells

Next-generation sequencing: introduction and genomicapplications

Quantitative transcriptomics: qRT-PCR

Advanced transcriptomics: gene expression microarrays

Next-generation sequencing in transcriptomics: RNA-seqexperiments

Analysis of transcription factor binding

8 15


4 30

Total: 40 100Practical:Skills to be developed:Intellectual skills:Students will be able to:Explore bioinformatics from computing perspective.Apply data mining techniques to provide better health care services.Explore and extract hidden information from bio informatics databases.

List of Practical: Hands-on experiments related to the course contentsAssignments:Based on the curriculum as covered by subject teacher.


PublisherRobert Gentleman R Programming for

BioinformaticsCRC Press

Reference Books:Arthur M. Lesk Introduction to

bioinformatics978-0199651566 Oxford University

Press

http://bioinfoacademy.com/HighThroughputR

http://www.crcpress.com/product/isbn/9781420063677

https://global.oup.com/academic/product/9780198794141



74

Sunil Mathur

StatisticalBioinformatics with R

9780123751041 Elesevier


1. Computer

2.





TotalMarks


Toanswer

Marksperquestion

TotalMarks

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1 to 5

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A All 1 10 10B All 5 5 3C All 15 3 3Examination Scheme for Practical Sessional examination:Practical Internal Sessional Continuous EvaluationInternal Examination:Continuous evaluation 40External Examination: Examiner-Signed Lab Note Book 10



75

On Spot Experiment 40Viva voce 10 60

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Information and Coding TheoryCourse Code: BITDSD502B Semester: VDuration: 48 MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial: 1 Attendance : 5Practical:0 Continuous Assessment:25Credit: 6 Practical Sessional internal continuous evaluation: NA


1. The aim of this course is to provide a basic understanding of the nature of information,the effects of noise in analogue and digital transmission systems and the constructionof both source codes and error-detection/-correction codes.

Objective:Sl. No.

1 To equip students with the basic understanding of the fundamental concept of sourcecoding, error correction and information as they are used in communications.

2 To enhance knowledge of probabilities, entropy and measures of information.3 To guide the student through the implications and consequences of information theoryand coding theory with reference to the application in modern communication andcomputer systems.


1 Strong mathematical knowledge on probability and abstract algebra.2 And the ability to understand newmathematical concepts as needed.

Contents 4 Hrs./weekChapter


01 Source Coding:Uncertainty and information, average mutual information andentropy, information measures for continuous random variables,source coding theorem, Huffman codes.

7 10

02 Channel Capacity And Coding:Channel models, channel capacity, channel coding, informationcapacity theorem, The Shannon limit.

12 20



76

03 Linear And Block Codes For Error Correction:Matrix description of linear block codes, equivalent codes, paritycheck matrix, decoding of a linear block code, perfect codes,Hamming codes.

12 20

04 Cyclic Codes:Polynomials, division algorithm for polynomials, a method forgenerating cyclic codes, matrix description of cyclic codes, Golaycodes. BCH Codes Primitive elements, minimal polynomials,generator polynomials in terms of minimal polynomials, examplesof BCH codes.

7 10

05 Convolutional CodesTree codes, trellis codes, polynomial description of convolutionalcodes, distance notions for convolutional codes, the generatingfunction, matrix representation of convolutional codes, decoding ofconvolutional codes, distance and performance bounds forconvolutional codes, examples of convolutional codes, Turbocodes, Turbo decoding.

10 10


4 30

Total: 52 100


PublisherRanjan Bose Information theory,

coding andcryptography

TMH

N Abramson Information andCoding

McGraw Hil

Reference Books:MMansurpur Introduction to

Information TheoryMcGraw Hill

R B Ash Information Theory Prentice Hall.






77


TotalMarks


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C

1 to 5

1 to 5

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1010

5

5

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60






A All 1 10 10B All 5 5 3C All 15 3 3

.

.

.

.

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Predictive & Prognostic AnalyticsCourse Code: BITDSD502C Semester: VDuration: 36 MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial: 1 Attendance : 5Practical:0 Continuous Assessment:25Credit: 6 Practical Sessional internal continuous evaluation: NA


1 Understand the process of formulating business objectives, data selection/collection,preparation and process to successfully design, build, evaluate and implementpredictive models for a various business application.

2 Compare the underlying predictive modeling techniques.

3 Select appropriate predictive modeling approaches to identify cases to progress with.

4 Apply predictive modeling approaches using a suitable package such as SPSS Modeler

Objective:Sl. No.



78

1. To learn, how to develop models to predict categorical and continuous outcomes, usingsuch techniques as neural networks, decision trees, logistic regression, support vectormachines and Bayesian network models.

2. To know the use of the binary classifier and numeric predictor nodes to automatemodel selection.

3. To advice on when and how to use each model. Also learn how to combine two or moremodels to improve prediction


1. Analytical skill



01 Introduction to Data Mining Introduction, what is Data Mining?Concepts of Data mining, Technologies Used, Data Mining Process,KDD Process Model, CRISP – DM, Mining on various kinds of data,Applications of Data Mining, Challenges of Data Mining.

8 10

02 Data Understanding and Preparation Introduction, Reading datafrom various sources, Data visualization, Distributions andsummary statistics, Relationships among variables, Extent ofMissing Data. Segmentation, Outlier detection, Automated DataPreparation, Combining data files, Aggregate Data, DuplicateRemoval, Sampling DATA, Data Caching, Partitioning data, MissingValues.

8 20

03 Model development & techniques Data Partitioning, Modelselection, Model Development Techniques, Neural networks,Decision trees, Logistic regression, Discriminant analysis, Supportvector machine, Bayesian Networks, Linear Regression, CoxRegression, Association rules.

10 20

04 Model Evaluation and Deployment Introduction, Model Validation,Rule Induction Using CHAID, Automating Models for Categoricaland Continuous targets, Comparing and Combining Models,Evaluation Charts for Model Comparison, MetaLevel Modeling,Deploying Model, Assessing Model Performance, Updating a Model.

10 20


4 30

Total: 40 100Assignments:Based on the curriculum as covered by subject teacher.List of BooksText Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the



79

PublisherPredictive & Advanced

AnalyticsIBM

Reference Books:Eric Siegel Predictive Analytics


1. Computer

2. Software R/Python





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Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Optimisation Techniques in Data AnalysisCourse Code: BITDSD502D Semester:VDuration: 48 MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial: 1 Attendance : 5Practical:0 Continuous Assessment:25Credit: 6 Practical Sessional internal continuous evaluation:NA

Practical Sessional external examination:NAAim:Sl. No.1. The aim of this course is to provide a basic understanding of the Optimisation

TechniquesObjective:Sl. No.

1 To impart knowledge in concepts and tools of Operations Research2 To understand mathematical models used in Operations Research3 To apply these techniques constructively to make effective business decisions


1 Strong mathematical background.2 And the ability to understand newmathematical concept as needed.



01 Introduction to Operation Research: Operation Researchapproach, scientific methods, introduction to models and modelingtechniques, general methods for Operation Research models,methodology and advantages of Operation Research, history ofOperation Research.

3 5

02 Linear Programming (LP): Introduction to LP and formulation ofLinear Programming problems, Graphical solution method,alternative or multiple optimal solutions, Unbounded solutions,Infeasible solutions, Maximization – Simplex Algorithm,Minimization – Simplex Algorithm using Big-Mmethod, Two phasemethod, Duality in linear programming, Integer linearprogramming.

8 10

03 Transportation & Assignment Problems: Introduction toTransportation problems, various methods of Transportation

7 10



81

problem, Variations in Transportation problem, introduction toAssignment problems, variations in Assignment problems.

04 Network Analysis: Network definition and Network diagram,probability in PERT analysis, project time cost trade off,introduction to resource smoothing and allocation.

710

05 Sequencing: Introduction, processing N jobs through twomachines, processing N jobs through three machines, processing Njobs through mmachines.

4 5

06 Inventory Model: Introduction to inventory control, deterministicinventory model, EOQ model with quantity discount.

4 5

07 Queuing Models: Concepts relating to queuing systems, basicelements of queuing model, role of Poison & exponentialdistribution, concepts of birth and death process.

7 10

08 Replacement &Maintenance Models: Replacement of items,subject to deterioration of items subject to random failure groupvs. individual replacement policies.

4 5

09 Simulation: Introduction & steps of simulation method,distribution functions and random number generation.

4 10


4 30

Total: 52 100



PublisherJ K Sharma Operations

Research Theory andApplications

MacMillan IndiaLtd

N D Vohra QuantitativeTechniques inmanagement

Tata McGraw Hill

Reference Books:Handy A Taha Operations Research –

An IntroductionPrentice Hall of India,New Delhi.

Wagner H M Principles ofOperations Research:With Applications toManagement Decisions

Prentice-Hall of India,New Delhi.

Hillier F S and Operations Research Holden Day Inc., San



82

Lieberman G J FranciscoPayne T A Quantitative

Techniques forManagement: A

Practical Approach

Reston Publishing Co.Inc., Virginia.





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Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Industrial Training and InternshipCourse Code: BITDSS501 Semester: VDuration: 0 MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 0 End Semester Exam: 100Tutorial: 0 Attendance: 0Practical: 0 Continuous Assessment: 0Credit: 2 Practical Sessional internal continuous evaluation: NA

Practical Sessional external examination: NAContents



83

Students are encouraged to go to Industrial Training/Internship for at least 2-3 months duringsemester break.

Semester VISl.No.

CBCSCategory


Theory1 CC-13 BITDSC601

BITDSC691Cloud Computing 4 0 4 6


Computer Vision & ImageProcessing

4 0 4 6

3 DSE-4 BITDSD601 Elective-III [MOOCS]A. Machine Learning for

Financial Modelling andForecasting

4/5

0/1

4/0

6

B. Machine Learning forIndustrial Application

C. Big Data Analytics(Hadoop)Sessional

4 SEC-5 BITCSS601 Grand Viva 0 0 2 15 SEC-6 BITCSS602 Seminar 0 2 0 26 DSE-5 BITCSD681 Major Project & Entrepreneurship II 0 0 8 4

Total Credit 25

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Cloud ComputingCourse Code: BITDSC601 &BITDSC691

Semester: VI


Practical Sessional external examination:60Aim: The main aim of this subject to enhance student knowledge with following conceptSl. No.1. Core concepts of the cloud computing2. Concepts in cloud infrastructures3. Concepts of cloud storage



84

4. Cloud programming modelsObjective:Sl. No.1. To learn how to use Cloud Services.2. To implement Virtualization3. To implement Task Scheduling algorithms.4. Understand the impact of engineering on legal and societal issues involved and

different security aspect.Pre-Requisite:Sl. No.1. Knowledge of computer systems, programming and debugging, with a strong

competency in at least one language (such as Java/Python), and the ability to pick upother languages as needed.

Contents Hrs./weekChapter Name of the Topic Hours Marks01 Definition of Cloud Computing and its Basics

Defining a Cloud, Cloud Types – NIST model, Cloud Cube model,Deployment models (Public , Private, Hybrid and CommunityClouds), Service Platform as a Service, Software as a Service withexamples of services/ service providers, models – Infrastructureas a Service, Cloud Reference model, Characteristics of CloudComputing – a shift in paradigm Benefits and advantages of CloudComputing, A brief introduction on Composability, Infrastructure,Platforms, Virtual Appliances, Communication Protocols,Applications, Connecting to the Cloud by Clients, IaaS – Basicconcept, Workload, partitioning of virtual private serverinstances, Pods, aggregations, silos PaaS – Basic concept, toolsand development environment with examplesSaaS - Basic concept and characteristics, Open SaaS andSOA, examples of SaaS platform Identity as a Service(IDaaS)Compliance as a Service (CaaS)

6 15

02 Use of Platforms in Cloud ComputingConcepts of Abstraction and VirtualizationVirtualization technologies : Types of virtualization (access,application, CPU, storage), Mobility patterns (P2V, V2V, V2P,P2P, D2C, C2C, C2D, D2D) Load Balancing and Virtualization:Basic Concepts, Network resources for load balancing,Advanced load balancing (including Application DeliveryController and Application Delivery Network), Mention of TheGoogle Cloud as an example of use of load balancingHypervisors: Virtual machine technology and types, VMwarevSphere Machine Imaging (including mention of Open

14 20



85

Virtualization Format – OVF)Porting of applications in the Cloud: The simple Cloud API andAppZero Virtual Application appliance,Concepts of Platform asa Service, Definition of services, Distinction between SaaS andPaaS (knowledge of Salesforce.com and Force.com),Application developmentUse of PaaS Application frameworks, Discussion of GoogleApplications Portfolio – Indexed search, Dark Web, Aggregationand disintermediation, Productivity applications and service,Adwords, Google Analytics, Google Translate, a brief discussionon Google Toolkit (including introduction of Google APIs inbrief), major features of Google App Engine service., Discussionof Google Applications Portfolio – Indexed search, Dark Web,Aggregation and disintermediation, Productivity applicationsand service, Adwords, Google Analytics, Google Translate, abrief discussion on Google Toolkit (including introduction ofGoogle APIs in brief), major features of Google App Engineservice, Windows Azure platform: Microsoft’s approach,architecture, and main elements, overview of Windows AzureAppFabric, Content Delivery Network, SQL Azure, and WindowsLive services,

03 Cloud InfrastructureCloud Management:An overview of the features of network management systemsand a brief introduction of related products from large cloudvendors, Monitoring of an entire cloud computing deploymentstack – an overview with mention of some products, Lifecyclemanagement of cloud services (six stages of lifecycle).Concepts of Cloud Security:Cloud security concerns, Security boundary, Security serviceboundary Overview of security mapping Security of data:Brokered cloud storage access, Storage location and tenancy,encryption, and auditing and complianceIdentity management (awareness of Identity protocol standards)

8 20

04 Concepts of Services and ApplicationsService Oriented Architecture: Basic concepts of message-basedtransactions, Protocol stack for an SOA architecture, Event-driven SOA, Enterprise Service Bus, Service catalogs,Applications in the Cloud: Concepts of cloud transactions,functionality mapping, Application attributes, Cloud serviceattributes, System abstraction and Cloud Bursting, Applicationsand Cloud APIsCloud-based Storage: Cloud storage definition – Manned andUnmanned

8 15



86

Webmail Services: Cloud mail services including Google Gmail,Mail2Web, Windows Live Hotmail, Yahoo mail, concepts ofSyndication servicesSub Total: 36 70Internal Assessment Examination & Preparation ofSemester Examination

4 30


1. Students are able to develop different algorithms related to CloudComputing.

2. Students are able to assess cloud Storage systems and Cloud security, the risks involved,its impact and develop cloud application.

3.

List of Practical: Hands-on experiments related to the course contentsAssignments:Based on the curriculum as covered by subject teacher.


PublisherBarrie Sosinsky Cloud Computing Bible 2013 Wiley India Pvt. LtdRajkumar

Buyya ,ChristianVecchiola, S.Thamarai Selvi

Mastering CloudComputing

2013 McGraw HillEducation (India)Private Limited

Reference Books:Anthony T. Velte Cloud computing: A

practical approachTata Mcgraw-Hill

Dr. Kumar Saurabh Cloud Computing Wiley IndiaMoyer Building applications

in cloud:Concept,Patterns and Projects

Pearson


1. Computer withmoderate configuration with high speed internetconnection

2. Python , java,End Semester Examination Scheme. MaximumMarks-70. Time allotted-3hrs.



87

Group Unit Objective Questions(MCQ only with thecorrect answer)



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Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Computer Vision & Image ProcessingCourse Code: BITDSC602 &BITDSC692

Semester: VI




88


1. Students will learn basic principles of image formation, image processing algorithmsand different algorithms for reconstruction and recognition from single or multipleimages

Objective:Sl. No.

1. To implement fundamental image processing techniques required for computer vision

2. Understand Image formation process

3. Extract features form Images and do analysis of Images

To develop applications using computer vision techniquesPre-Requisite:Sl. No.

1. Programming

2. Mathematic course



01 Overview, computer imaging systems, lenses, Image formation andsensing, Image analysis, pre-processing and Binary image analysis

3 10

02 Edge detection, Edge detection performance, Hough transform,corner detection

6 10

03 Segmentation, Morphological filtering, Fourier transform 3 1004 Feature extraction, shape, histogram, color, spectral, texture, using

CVIPtools, Feature analysis, feature vectors, distance /similaritymeasures, data preprocessing

9 10

05 Pattern Analysis:Clustering: K-Means, K-Medoids, Mixture of GaussiansClassification: Discriminant Function, Supervised, Un-supervised,SemisupervisedClassifiers: Bayes, KNN, ANNmodels; Dimensionality Reduction:PCA, LDA, ICA, and Non-parametric methods.

9 20

06 Recent trends in Activity Recognition, computational photography,Biometrics

6 10


4 30

Total: 40 100Practical:



89


1. Ability to pre process the image

2. Ability to image feature identification

3. Can be able to apply recent machine learning methods for differentpurpose.

List of Practical:Based on theory PaperAssignments:Based on the curriculum as covered by subject teacher.


PublisherRichard Szeliski Computer Vision:

Algorithms andApplications

Goodfellow, Bengio,and Courville

Deep Learning

Reference Books:Fisher et al . Dictionary of

Computer Vision andImage Processing


1. Computer

2. Matlab/python/R





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● Only multiple choice type question (MCQ) with one correct answer are to be set inthe



90

objective part.






Name of the Course: B.Sc. in Information Technology (Data Science)Subject:Machine Learning for Financial Modelling and ForecastingCourse Code: BITDSD601A Semester: VIDuration: 36 MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial: 1 Attendance : 5Practical:0 Continuous Assessment:25Credit: 6 Practical Sessional internal continuous evaluation:NA


1. Aim of this study to predict supply/demand/inventory of the market, and improvebusiness performance.

Objective:Sl. No.

1. To acquire expertise in the mechanics of the most popular machine learning models,and their inter-relationship, in order to do proper model selection and fitting.

2. To understand the behavior of financial time series, their statistical properties, andlearn to design and assess financial forecasting models and investment strategies basedon supervised learning models or other models that use different types (quantitativeand qualitative) of information sets.

Pre-Requisite:



91

Sl. No.1. Foundations of Data Science. Basic Statistics.

2. Knowledge of R or Python



01 Understanding Financial Time Series Data:Asset’s price and return. Basic statistics of returns. Measures ofdependence. Stationarity. Forecasting. Volatility. Technical andFundamental Financial indicators as information set.

8 15

02 Financial Time Series Modeling:Linear regression models and GARCH nonlinear model (quickreview). Kernels in Statistical Machine Learning. Support VectorRegression. Neural Networks. Feed-forward networks.Multilayered Networks (Deep Learners). Recurrent networks.LSTM. Data preprocessing and Evaluation of Model Estimation.

10 20

03 OptimizationHeuristics in Finance. Random search. Simulated Annealing,Genetic Programming, and other heuristics. Using heuristics forparameter estimation of GARCH, SVM, and Neural networks.

8 15

04 ApplicationsEstimating and Forecasting Financial time series. Algorithmictrading. Porfolio selection. Portfolio optimization under differentconstraints sets. Credit scoring.

10 20


4 30

Total: 40 100


PublisherA. Arratia Computational

Finance, AnIntroductory Course

with R

Atlantis Press &Springer, 2014

P. Cortez Modern Optimizationwith R

2014

Reference Books:R. Tsay Analysis of Financial Wiley, 2013



92

Time SeriesCover, T. A., andThomas, J. A.,

Elements ofInformation Theory

Second ed. (Wiley, 2006).





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Name of the Course: B.Sc. in Information Technology (Data Science)Subject:Machine Learning for Industrial ApplicationCourse Code: BITDSD601B Semester: VIDuration: 36 MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam: 70Tutorial: 1 Attendance : 5Practical:0 Continuous Assessment:25Credit: 6 Practical Sessional internal continuous evaluation:NA




93

.1 Familiarity with vision and medical image computing based on machine learningapproach.

Objective:Sl. No.1. Each student will gain an understanding of the breadth of methods used in medical

image segmentation2. Each student will gain a detailed understanding of one particular approach.


1. Digital image processing

2. Mathematical Knowledge



01 IntroductionSimilarity between images. Image preprocessing. Image matchingand registration. Basics.Advanced image registration techniques. Applications of imageregistration. Evaluating imageregistration for medical applications.

9 20

02 Medical Image Segmentation and Applications:Introduction to Computer Aided Detection (CADe). Imagepreprocessing. Clustering segmentation techniques. Region-basedsegmentation in 2D and 3D images. Free-formSegmentation and active contours. Deformable template matchingand active shape models.Evaluation of detection algorithms for medical applications

12 25

03 Computer Aided Diagnosis:Introduction to diagnosis and CADx. Object and imagecharacterization. Morphological, texture,and shape descriptors. Interest point detectors and descriptors.Classification and diagnosis.CADx evaluation. Applications through machine learning.

15 25


4 30

Total: 40 100Practical:Skills to be developed:Assignments:Based on the curriculum as covered by subject teacher.List of Books



94

Text Books:Name of Author Title of the Book Edition/ISSN/ISBN Name of the

PublisherRafael C. Gonzalez Digital Image

Processing UsingMATLAB

978-0130085191

Oleg S.Pianykh (Author)

Digital Imaging andCommunications inMedicine (DICOM)

978-3540745709

Reference Books:Barton F. Branstetter Practical Imaging

Informatics:Foundations andApplications for PACS

978-1441904836

Bettyann H. Kevles Naked to the Bone





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https://www.amazon.com/s/ref%3Ddp_byline_sr_book_1?ie=UTF8&field-author=Barton%2BF.%2BBranstetter%2BIV&text=Barton%2BF.%2BBranstetter%2BIV&sort=relevancerank&search-alias=books

https://www.amazon.com/s/ref%3Ddp_byline_sr_book_1?ie=UTF8&field-author=Bettyann%2BH.%2BKevles&text=Bettyann%2BH.%2BKevles&sort=relevancerank&search-alias=books



95

B All 5 5 3C All 15 3 3

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Big Data Analytics(Hadoop)Course Code: BITDSD601C Semester: VIDuration: 36 Hrs MaximumMarks:100Teaching Scheme Examination SchemeTheory: 5 End Semester Exam:70Tutorial:1 Attendance: 5Practical: 0 Continuous Assessment: 25Credit: 6 Practical Sessional internal continuous evaluation: NA


1. Understand big data for business intelligence

2. Learn business case studies for big data analytics.

3. Understand nosql big data management.

4. Performmap-reduce analytics using Hadoop and related tools

Objective:Sl. No.

1. Understand the fundamentals of Big cloud and data architectures.

2. Understand HDFS file structure and Mapreduce frameworks, and use them to solvecomplex problems, which require massive computation power

3. Use relational data in a Hadoop environment, using Hive and Hbase tools of the HadoopEcosystem..

4. Understand the Comparison with traditional databases.


1. Database Management Systems.

2. Object Oriented Programming Through Java



01 Introduction to big data 6 10



96

Introduction to Big Data Platform – Challenges of ConventionalSystems - Intelligent data analysis – Nature of Data - AnalyticProcesses and Tools - Analysis vs Reporting.

02 Mining data streamsIntroduction To Streams Concepts – Stream Data Model andArchitecture - Stream Computing - Sampling Data in a Stream –Filtering Streams – Counting Distinct Elements in a Stream –Estimating Moments – Counting Oneness in a Window – DecayingWindow - Real time Analytics Platform(RTAP) Applications - CaseStudies - Real Time Sentiment Analysis- Stock Market Predictions.

10 20

03 HadoopHistory of Hadoop- the Hadoop Distributed File System –Components of Hadoop Analysing the Data with Hadoop- ScalingOut- Hadoop Streaming- Design of HDFS-Java interfaces to HDFSBasics- Developing a Map Reduce Application-How Map ReduceWorks-Anatomy of a Map Reduce Job run-Failures-Job Scheduling-Shuffle and Sort – Task execution - Map Reduce Types andFormats- Map Reduce FeaturesHadoop environment.

12 20

04 FrameworksApplications on Big Data Using Pig and Hive – Data processingoperators in Pig – Hive services – HiveQL – Querying Data in Hive- fundamentals of HBase and ZooKeeper - IBM InfoSphereBigInsights and Streams. Predictive Analytics- Simple linearregression- Multiple linear regression- Interpretation 5 ofregression coefficients. Visualizations - Visual data analysistechniques- interaction techniques - Systems and applications.

8 20


4 30

Total: 40 100Assignments:Based on the curriculum as covered by subject teacher.


PublisherTomWhite Hadoop: The Definitive

GuideThird Edition O’reilly Media

Chris Eaton, DirkDeRoos, Tom

Understanding BigData: Analytics for

McGrawHillPublishing



97

Deutsch, GeorgeLapis, PaulZikopoulos

Enterprise ClassHadoop and StreamingData

Reference Books:Anand Rajaramanand Jeffrey DavidUllman

Mining of MassiveDatasets

CUP

Bill Franks Taming the Big DataTidal Wave: FindingOpportunities in HugeData Streams withAdvanced Analytics

JohnWiley& sons

Glenn J. Myatt Making Sense of Data JohnWiley & SonsPete Warden Big Data Glossary O’ReillyList of equipment/apparatus for laboratory experiments:Sl. No.


7. Linux os or VM

8. Hadoop 2.x or higher and other software as required.





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B All 5 5 3C All 15 5 3Examination Scheme for Practical Sessional examination:

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: Grand Viva VoceCourse Code: BITCSS601 Semester: VIDuration: 24Hrs MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 0 End Semester Exam: 100Tutorial: 0 Attendance: 0Practical: 2 Continuous Assessment: 0Credit: 1 Practical Sessional internal continuous evaluation: NA

Practical Sessional external examination: NAContentsStudents will give a viva from all the subject that they have covered in the course.

Name of the Course: B.Sc. in Information Technology (Data Science)Subject: SeminarCourse Code: BITCSS602 Semester: VIDuration: MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 0 End Semester Exam: 100Tutorial: 2 Attendance: 0Practical: 0 Continuous Assessment: 0Credit: 2 Practical Sessional internal continuous evaluation: 40

Practical Sessional external examination: 60ContentsStudents will present a presentation on application areas of latest technologies and currenttopics of societal relevance.

Name of the Course: B.Sc. in Information Technology (Data Science)Subject:Major Project & Entrepreneurship IICourse Code: BITDS682 Semester: VIDuration: 36 Hrs. MaximumMarks: 100Teaching Scheme Examination SchemeTheory: 0 End Semester Exam: 100



99

Tutorial: 0 Attendance: 0Practical: 8 Continuous Assessment: 0Credit: 4 Practical Sessional internal continuous evaluation: 40




100

departmentofinformationtechnology b.sc

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